Implicit identification of translation payload with neural machine translation

ABSTRACT

Systems and processes for operating an electronic device to train a machine-learning translation system are described. In one process, a first set of training data is obtained. The first set of training data includes at least one payload in a first language and a translation of the at least one payload in a second language. The process further includes obtaining one or more templates for adapting the at least one payload; adapting the at least one payload using the one or more templates to generate at least one adapted payload formulated as a translation request; generating a second set of training data based on the at least one adapted payload; and training the machine-learning translation system using the second set of training data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/650,969, entitled “IMPLICIT IDENTIFICATION OF TRANSLATION PAYLOADWITH NEURAL MACHINE TRANSLATION,” filed on Mar. 30, 2018, the content ofwhich is incorporated by reference in its entirety for all purposes.

FIELD

This relates generally to digital assistants and, more specifically, toproviding machine-learning based translation by digital assistants.

BACKGROUND

Digital assistants (or virtual assistants or intelligent automatedassistants) can provide a beneficial human-machine interface. Suchassistants can allow users to interact with devices or systems usingnatural language in spoken and/or text forms. For example, a user canprovide a speech input containing a user request to a digital assistantoperating on an electronic device. The virtual assistant can interpretthe user's intent from the speech input and operationalize the user'sintent into tasks. The tasks can then be performed by executing one ormore services of the electronic device, and a relevant output responsiveto the user request can be returned to the user.

Digital assistants are frequently used for translation from one languageto another. Current translation techniques require the digital assistantto perform multiple steps including determining that a user request is atranslation request, identifying the translation payload, identifyingthe target translation language, and obtaining the translation of thepayload in the target translation language. Accurately and correctlyidentifying the translation payload may sometimes be difficult using thecurrent techniques due to the variations of user requests. Further,current translation techniques require performing multiple stepsincluding speech recognition, natural language understanding (e.g.,intent interpretation and parsing), and flow execution. The error of thetranslation is thus a multiplication of the errors associated withmultiple steps. Thus, a single end-to-end system that can directlyprocess the entire translation request without the need to explicitlyidentify the payload is desirable.

SUMMARY

Systems and processes for training a machine-learning translation systemof a digital assistant and for performing translations using the trainedmachine-learning translation system are provided.

In accordance with one or more examples, a method includes, at anelectronic device with one or more processors, memory, and a microphone,obtaining a first set of training data. The first set of training dataincludes at least one payload in a first language and a translation ofthe at least one payload in a second language. The method furtherincludes obtaining one or more templates for adapting the at least onepayload; adapting the at least one payload using the one or moretemplates to generate at least one adapted payload formulated as atranslation request; generating a second set of training data based onthe at least one adapted payload; and training the machine-learningtranslation system using the second set of training data.

Example non-transitory computer-readable media are disclosed herein. Anexample non-transitory computer-readable storage medium stores one ormore programs. The one or more programs comprise instructions, whichwhen executed by one or more processors of an electronic device, causethe electronic device to obtain a first set of training data. The firstset of training data includes at least one payload in a first languageand a translation of the at least one payload in a second language. Theone or more programs comprise further instructions that cause theelectronic device to obtain one or more templates for adapting the atleast one payload; adapt the at least one payload using the one or moretemplates to generate at least one adapted payload formulated as atranslation request, generate a second set of training data based on theat least one adapted payload; and train the machine-learning translationsystem using the second set of training data.

Example electronic devices are disclosed herein. An example electronicdevice comprises one or more processors; a memory; and one or moreprograms, where the one or more programs are stored in the memory andconfigured to be executed by the one or more processors, the one or moreprograms including instructions for obtaining a first set of trainingdata. The first set of training data includes at least one payload in afirst language and a translation of the at least one payload in a secondlanguage. The one or more programs further includes instructions forobtaining one or more templates for adapting the at least one payload;adapting the at least one payload using the one or more templates togenerate at least one adapted payload formulated as a translationrequest; generating a second set of training data based on the at leastone adapted payload; and training the machine-learning translationsystem using the second set of training data.

An example electronic device comprises means for obtaining a first setof training data. The first set of training data includes at least onepayload in a first language and a translation of the at least onepayload in a second language. The electronic device further includesmeans for obtaining one or more templates for adapting the at least onepayload; means for adapting the at least one payload using the one ormore templates to generate at least one adapted payload formulated as atranslation request; means for generating a second set of training databased on the at least one adapted payload; and means for training themachine-learning translation system using the second set of trainingdata.

In accordance with one or more examples, a method includes, at anelectronic device with one or more processors, memory, and a microphone,receiving, via the microphone, a speech input. The method furtherincludes, in response to receiving the speech input, determining whetherthe received speech input represents a user request for translation andproviding a representation of the received speech input to themachine-learning translation system trained with a set of training datain accordance with a determination that the received speech inputrepresents a user request for translation. The set of training datacomprises at least one adapted payload formulated as a translationrequest. The method further includes obtaining, using the trainedmachine-learning translation system, a response to the user request fortranslation based on the representation of the received speech input;and providing an audio output corresponding to the obtained response tothe user request for translation.

Example non-transitory computer-readable media are disclosed herein. Anexample non-transitory computer-readable storage medium stores one ormore programs. The one or more programs comprise instructions, whichwhen executed by one or more processors of an electronic device, causethe electronic device to receive, via the microphone, a speech input.The one or more programs further includes instructions that cause theelectronic device to, in response to receiving the speech input,determine whether the received speech input represents a user requestfor translation and provide a representation of the received speechinput to the machine-learning translation system trained with a set oftraining data in accordance with a determination that the receivedspeech input represents a user request for translation. The set oftraining data comprises at least one adapted payload formulated as atranslation request. The one or more programs further includesinstructions that cause the electronic device to obtain, using thetrained machine-learning translation system, a response to the userrequest for translation based on the representation of the receivedspeech input; and provide an audio output corresponding to the obtainedresponse to the user request for translation.

Example electronic devices are disclosed herein. An example electronicdevice comprises one or more processors; a memory; and one or moreprograms, where the one or more programs are stored in the memory andconfigured to be executed by the one or more processors, the one or moreprograms including instructions for receiving, via the microphone, aspeech input. The one or more programs further include instructions for,in response to receiving the speech input, determining whether thereceived speech input represents a user request for translation andproviding a representation of the received speech input to themachine-learning translation system trained with a set of training datain accordance with a determination that the received speech inputrepresents a user request for translation. The set of training datacomprises at least one adapted payload formulated as a translationrequest. The one or more programs further includes instructions forobtaining, using the trained machine-learning translation system, aresponse to the user request for translation based on the representationof the received speech input; and providing an audio outputcorresponding to the obtained response to the user request fortranslation.

An example electronic device comprises means for receiving, via themicrophone, a speech input. The electronic device further includes, inresponse to receiving the speech input, means for determining whetherthe received speech input represents a user request for translation andmeans for providing a representation of the received speech input to themachine-learning translation system trained with a set of training datain accordance with a determination that the received speech inputrepresents a user request for translation. The set of training dataincludes at least one adapted payload formulated as a translationrequest. The electronic device further includes means for obtaining,using the trained machine-learning translation system, a response to theuser request for translation based on the representation of the receivedspeech input, and means for providing an audio output corresponding tothe obtained response to the user request for translation.

As described above, conventional digital assistant techniques forfacilitating translation typically parse the user input to explicitlyidentify a payload and a target translation language. For example, afterreceiving a user input such as “How do I say ‘Good morning’ in German?”,a conventional digital assistant typically parses the user input toidentify the payload (e.g., “Good morning”) and a target translationlanguage (e.g., “German”). A payload is a word, phrase, or sentenceto-be-translated. A conventional digital assistant, however, may notaccurately or correctly identify the payload from a user input. Forexample, a user input may not always include an explicit translationrequest such as “How do I say in Spanish: ‘I would like to have aMargarita please.’” Instead, the user input may include an implicittranslation request such as “How do I order a Margarita in Spanish?” Asa result, the current digital assistant techniques may incorrectlyidentify the payload as “order Margarita,” rather than “I would like tohave a Margarita please.” Conventional rigid translation systems maythus fail to identify the payload or may incorrectly or inaccuratelyidentify a payload.

Various techniques for training and using a machine-learning translationsystem described in this application do not require explicitlyidentifying of the payload in a user request for translation. Instead,the machine-learning translation system is trained with training dataincluding adapted payloads formulated in the form of translationrequests. As a result, the machine-learning translation systems arecapable of accurately performing translation regardless of whether theuser requests include explicit payloads. Such systems do not requireexplicitly identifying the payload for providing a translation.Moreover, the machine-learning translation systems can be continuouslyor periodically trained to incorporate additional types of userrequests, thereby further improving the accuracy of translation. Themachine-learning translation systems described in this application thusprovide an end-to-end solution that enables a direct processing of theentire user request to provide the required translation without havingto parse the user request to explicitly identify the payload.

Furthermore, various techniques for training the machine-learningsystems and performing translation based on trained machine-learningsystems described in this application enhance the operability of thedevice and makes the user-device interface more efficient (e.g., by notrequiring the user to explicitly include the payload in the request fortranslation) which, additionally, reduces power usage and improvesbattery life of the device by enabling the user to use the device morequickly and efficiently.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system and environment forimplementing a digital assistant, according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction deviceimplementing the client-side portion of a digital assistant, accordingto various examples.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling, according to various examples.

FIG. 3 illustrates a portable multifunction device implementing theclient-side portion of a digital assistant, according to variousexamples.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface, according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on a portable multifunction device, according to variousexamples.

FIG. 5B illustrates an exemplary user interface for a multifunctiondevice with a touch-sensitive surface that is separate from the display,according to various examples.

FIG. 6A illustrates a personal electronic device, according to variousexamples.

FIG. 6B is a block diagram illustrating a personal electronic device,according to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or aserver portion thereof, according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG.7A, according to various examples.

FIG. 7C illustrates a portion of an ontology, according to variousexamples.

FIG. 8 illustrates a block diagram of an exemplary digital assistant forperforming machine-learning based translation.

FIGS. 9A-9C illustrate an exemplary digital assistant for training amachine-learning translation system.

FIGS. 10A-10D illustrate an exemplary digital assistant for performingtranslation using a trained machine-learning translation system.

FIGS. 11A-11B illustrate a process for training a machine-learningtranslation system, according to various embodiments.

FIGS. 12A-12D illustrate a process for performing translation using atrained machine-learning translation system, according to variousembodiments.

DETAILED DESCRIPTION

In the following description of embodiments, reference is made to theaccompanying drawings in which are shown by way of illustration specificexamples that can be practiced. It is to be understood that otherexamples can be used and structural changes can be made withoutdeparting from the scope of the various examples.

Although the following description uses terms “first,” “second,” etc. todescribe various elements, these elements should not be limited by theterms. These terms are only used to distinguish one element fromanother. For example, a first input could be termed a second input, and,similarly, a second input could be termed a first input, withoutdeparting from the scope of the various described examples. The firstinput and the second input are both inputs and, in some cases, areseparate and different inputs.

The terminology used in the description of the various describedexamples herein is for the purpose of describing particular examplesonly and is not intended to be limiting. As used in the description ofthe various described examples and the appended claims, the singularforms “a,” “an,” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will also beunderstood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to variousexamples. In some examples, system 100 implements a digital assistant.The terms “digital assistant,” “virtual assistant,” “intelligentautomated assistant,” or “automatic digital assistant” refer to anyinformation processing system that interprets natural language input inspoken and/or textual form to infer user intent, and performs actionsbased on the inferred user intent. For example, to act on an inferreduser intent, the system performs one or more of the following:identifying a task flow with steps and parameters designed to accomplishthe inferred user intent, inputting specific requirements from theinferred user intent into the task flow; executing the task flow byinvoking programs, methods, services, APIs, or the like; and generatingoutput responses to the user in an audible (e.g., speech) and/or visualform.

Specifically, a digital assistant is capable of accepting a user requestat least partially in the form of a natural language command, request,statement, narrative, and/or inquiry. Typically, the user request seekseither an informational answer or performance of a task by the digitalassistant. A satisfactory response to the user request includes aprovision of the requested informational answer, a performance of therequested task, or a combination of the two. For example, a user asksthe digital assistant a question, such as “Where am I right now?” Basedon the user's current location, the digital assistant answers, “You arein Central Park near the west gate.” The user also requests theperformance of a task, for example, “Please invite my friends to mygirlfriend's birthday party next week.” In response, the digitalassistant can acknowledge the request by saying “Yes, right away,” andthen send a suitable calendar invite on behalf of the user to each ofthe user's friends listed in the user's electronic address book. Duringperformance of a requested task, the digital assistant sometimesinteracts with the user in a continuous dialogue involving multipleexchanges of information over an extended period of time. There arenumerous other ways of interacting with a digital assistant to requestinformation or performance of various tasks. In addition to providingverbal responses and taking programmed actions, the digital assistantalso provides responses in other visual or audio forms, e.g., as text,alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant is implementedaccording to a client-server model. The digital assistant includesclient-side portion 102 (hereafter “DA client 102”) executed on userdevice 104 and server-side portion 106 (hereafter “DA server 106”)executed on server system 108. DA client 102 communicates with DA server106 through one or more networks 110. DA client 102 provides client-sidefunctionalities such as user-facing input and output processing andcommunication with DA server 106. DA server 106 provides server-sidefunctionalities for any number of DA clients 102 each residing on arespective user device 104.

In some examples, DA server 106 includes client-facing I/O interface112, one or more processing modules 114, data and models 116, and I/Ointerface to external services 118. The client-facing I/O interface 112facilitates the client-facing input and output processing for DA server106. One or more processing modules 114 utilize data and models 116 toprocess speech input and determine the user's intent based on naturallanguage input. Further, one or more processing modules 114 perform taskexecution based on inferred user intent. In some examples, DA server 106communicates with external services 120 through network(s) 110 for taskcompletion or information acquisition. I/O interface to externalservices 118 facilitates such communications.

User device 104 can be any suitable electronic device. In some examples,user device is a portable multifunctional device (e.g., device 200,described below with reference to FIG. 2A), a multifunctional device(e.g., device 400, described below with reference to FIG. 4), or apersonal electronic device (e.g., device 600, described below withreference to FIG. 6A-B.) A portable multifunctional device is, forexample, a mobile telephone that also contains other functions, such asPDA and/or music player functions. Specific examples of portablemultifunction devices include the iPhone®, iPod Touch®, and iPad®devices from Apple Inc. of Cupertino, Calif. Other examples of portablemultifunction devices include, without limitation, laptop or tabletcomputers. Further, in some examples, user device 104 is a non-portablemultifunctional device. In particular, user device 104 is a desktopcomputer, a game console, a television, or a television set-top box. Insome examples, user device 104 includes a touch-sensitive surface (e.g.,touch screen displays and/or touchpads). Further, user device 104optionally includes one or more other physical user-interface devices,such as a physical keyboard, a mouse, and/or a joystick. Variousexamples of electronic devices, such as multifunctional devices, aredescribed below in greater detail.

Examples of communication network(s) 110 include local area networks(LAN) and wide area networks (WAN), e.g., the Internet. Communicationnetwork(s) 110 is implemented using any known network protocol,including various wired or wireless protocols, such as, for example,Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or anyother suitable communication protocol.

Server system 108 is implemented on one or more standalone dataprocessing apparatus or a distributed network of computers. In someexamples, server system 108 also employs various virtual devices and/orservices of third-party service providers (e.g., third-party cloudservice providers) to provide the underlying computing resources and/orinfrastructure resources of server system 108.

In some examples, user device 104 communicates with DA server 106 viasecond user device 122. Second user device 122 is similar or identicalto user device 104. For example, second user device 122 is similar todevices 200, 400, or 600 described below with reference to FIGS. 2A, 4,and 6A-B. User device 104 is configured to communicatively couple tosecond user device 122 via a direct communication connection, such asBluetooth, NFC, BTLE, or the like, or via a wired or wireless network,such as a local Wi-Fi network. In some examples, second user device 122is configured to act as a proxy between user device 104 and DA server106. For example, DA client 102 of user device 104 is configured totransmit information (e.g., a user request received at user device 104)to DA server 106 via second user device 122. DA server 106 processes theinformation and return relevant data (e.g., data content responsive tothe user request) to user device 104 via second user device 122.

In some examples, user device 104 is configured to communicateabbreviated requests for data to second user device 122 to reduce theamount of information transmitted from user device 104. Second userdevice 122 is configured to determine supplemental information to add tothe abbreviated request to generate a complete request to transmit to DAserver 106. This system architecture can advantageously allow userdevice 104 having limited communication capabilities and/or limitedbattery power (e.g., a watch or a similar compact electronic device) toaccess services provided by DA server 106 by using second user device122, having greater communication capabilities and/or battery power(e.g., a mobile phone, laptop computer, tablet computer, or the like),as a proxy to DA server 106. While only two user devices 104 and 122 areshown in FIG. 1, it should be appreciated that system 100, in someexamples, includes any number and type of user devices configured inthis proxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 includes both aclient-side portion (e.g., DA client 102) and a server-side portion(e.g., DA server 106), in some examples, the functions of a digitalassistant are implemented as a standalone application installed on auser device. In addition, the divisions of functionalities between theclient and server portions of the digital assistant can vary indifferent implementations. For instance, in some examples, the DA clientis a thin-client that provides only user-facing input and outputprocessing functions, and delegates all other functionalities of thedigital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices forimplementing the client-side portion of a digital assistant. FIG. 2A isa block diagram illustrating portable multifunction device 200 withtouch-sensitive display system 212 in accordance with some embodiments.Touch-sensitive display 212 is sometimes called a “touch screen” forconvenience and is sometimes known as or called a “touch-sensitivedisplay system.” Device 200 includes memory 202 (which optionallyincludes one or more computer-readable storage mediums), memorycontroller 222, one or more processing units (CPUs) 220, peripheralsinterface 218, RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, input/output (I/O) subsystem 206, other input controldevices 216, and external port 224. Device 200 optionally includes oneor more optical sensors 264. Device 200 optionally includes one or morecontact intensity sensors 265 for detecting intensity of contacts ondevice 200 (e.g., a touch-sensitive surface such as touch-sensitivedisplay system 212 of device 200). Device 200 optionally includes one ormore tactile output generators 267 for generating tactile outputs ondevice 200 (e.g., generating tactile outputs on a touch-sensitivesurface such as touch-sensitive display system 212 of device 200 ortouchpad 455 of device 400). These components optionally communicateover one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of acontact on a touch-sensitive surface refers to the force or pressure(force per unit area) of a contact (e.g., a finger contact) on thetouch-sensitive surface, or to a substitute (proxy) for the force orpressure of a contact on the touch-sensitive surface. The intensity of acontact has a range of values that includes at least four distinctvalues and more typically includes hundreds of distinct values (e.g., atleast 256). Intensity of a contact is, optionally, determined (ormeasured) using various approaches and various sensors or combinationsof sensors. For example, one or more force sensors underneath oradjacent to the touch-sensitive surface are, optionally, used to measureforce at various points on the touch-sensitive surface. In someimplementations, force measurements from multiple force sensors arecombined (e.g., a weighted average) to determine an estimated force of acontact. Similarly, a pressure-sensitive tip of a stylus is, optionally,used to determine a pressure of the stylus on the touch-sensitivesurface. Alternatively, the size of the contact area detected on thetouch-sensitive surface and/or changes thereto, the capacitance of thetouch-sensitive surface proximate to the contact and/or changes thereto,and/or the resistance of the touch-sensitive surface proximate to thecontact and/or changes thereto are, optionally, used as a substitute forthe force or pressure of the contact on the touch-sensitive surface. Insome implementations, the substitute measurements for contact force orpressure are used directly to determine whether an intensity thresholdhas been exceeded (e.g., the intensity threshold is described in unitscorresponding to the substitute measurements). In some implementations,the substitute measurements for contact force or pressure are convertedto an estimated force or pressure, and the estimated force or pressureis used to determine whether an intensity threshold has been exceeded(e.g., the intensity threshold is a pressure threshold measured in unitsof pressure). Using the intensity of a contact as an attribute of a userinput allows for user access to additional device functionality that mayotherwise not be accessible by the user on a reduced-size device withlimited real estate for displaying affordances (e.g., on atouch-sensitive display) and/or receiving user input (e.g., via atouch-sensitive display, a touch-sensitive surface, or aphysical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output”refers to physical displacement of a device relative to a previousposition of the device, physical displacement of a component (e.g., atouch-sensitive surface) of a device relative to another component(e.g., housing) of the device, or displacement of the component relativeto a center of mass of the device that will be detected by a user withthe user's sense of touch. For example, in situations where the deviceor the component of the device is in contact with a surface of a userthat is sensitive to touch (e.g., a finger, palm, or other part of auser's hand), the tactile output generated by the physical displacementwill be interpreted by the user as a tactile sensation corresponding toa perceived change in physical characteristics of the device or thecomponent of the device. For example, movement of a touch-sensitivesurface (e.g., a touch-sensitive display or trackpad) is, optionally,interpreted by the user as a “down click” or “up click” of a physicalactuator button. In some cases, a user will feel a tactile sensationsuch as an “down click” or “up click” even when there is no movement ofa physical actuator button associated with the touch-sensitive surfacethat is physically pressed (e.g., displaced) by the user's movements. Asanother example, movement of the touch-sensitive surface is, optionally,interpreted or sensed by the user as “roughness” of the touch-sensitivesurface, even when there is no change in smoothness of thetouch-sensitive surface. While such interpretations of touch by a userwill be subject to the individualized sensory perceptions of the user,there are many sensory perceptions of touch that are common to a largemajority of users. Thus, when a tactile output is described ascorresponding to a particular sensory perception of a user (e.g., an “upclick,” a “down click,” “roughness”), unless otherwise stated, thegenerated tactile output corresponds to physical displacement of thedevice or a component thereof that will generate the described sensoryperception for a typical (or average) user.

It should be appreciated that device 200 is only one example of aportable multifunction device, and that device 200 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 2A areimplemented in hardware, software, or a combination of both hardware andsoftware, including one or more signal processing and/orapplication-specific integrated circuits.

Memory 202 includes one or more computer-readable storage mediums. Thecomputer-readable storage mediums are, for example, tangible andnon-transitory. Memory 202 includes high-speed random access memory andalso includes non-volatile memory, such as one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices. Memory controller 222 controls access to memory 202 byother components of device 200.

In some examples, a non-transitory computer-readable storage medium ofmemory 202 is used to store instructions (e.g., for performing aspectsof processes described below) for use by or in connection with aninstruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device and execute the instructions. In other examples,the instructions (e.g., for performing aspects of the processesdescribed below) are stored on a non-transitory computer-readablestorage medium (not shown) of the server system 108 or are dividedbetween the non-transitory computer-readable storage medium of memory202 and the non-transitory computer-readable storage medium of serversystem 108.

Peripherals interface 218 is used to couple input and output peripheralsof the device to CPU 220 and memory 202. The one or more processors 220run or execute various software programs and/or sets of instructionsstored in memory 202 to perform various functions for device 200 and toprocess data. In some embodiments, peripherals interface 218, CPU 220,and memory controller 222 are implemented on a single chip, such as chip204. In some other embodiments, they are implemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 208 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 208 optionally includes well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 208 optionally communicates with networks, such as theInternet, also referred to as the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The RF circuitry 208optionally includes well-known circuitry for detecting near fieldcommunication (NFC) fields, such as by a short-range communicationradio. The wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies, including but notlimited to Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), high-speed downlink packet access (HSDPA),high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO),HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), nearfield communication (NFC), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n,and/or IEEE 802.11 ac), voice over Internet Protocol (VoIP), Wi-MAX, aprotocol for e mail (e.g., Internet message access protocol (IMAP)and/or post office protocol (POP)), instant messaging (e.g., extensiblemessaging and presence protocol (XMPP), Session Initiation Protocol forInstant Messaging and Presence Leveraging Extensions (SIMPLE), InstantMessaging and Presence Service (IMPS)), and/or Short Message Service(SMS), or any other suitable communication protocol, includingcommunication protocols not yet developed as of the filing date of thisdocument.

Audio circuitry 210, speaker 211, and microphone 213 provide an audiointerface between a user and device 200. Audio circuitry 210 receivesaudio data from peripherals interface 218, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 211.Speaker 211 converts the electrical signal to human-audible sound waves.Audio circuitry 210 also receives electrical signals converted bymicrophone 213 from sound waves. Audio circuitry 210 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 218 for processing. Audio data are retrieved fromand/or transmitted to memory 202 and/or RF circuitry 208 by peripheralsinterface 218. In some embodiments, audio circuitry 210 also includes aheadset jack (e.g., 312, FIG. 3). The headset jack provides an interfacebetween audio circuitry 210 and removable audio input/outputperipherals, such as output-only headphones or a headset with bothoutput (e.g., a headphone for one or both ears) and input (e.g., amicrophone).

I/O subsystem 206 couples input/output peripherals on device 200, suchas touch screen 212 and other input control devices 216, to peripheralsinterface 218. I/O subsystem 206 optionally includes display controller256, optical sensor controller 258, intensity sensor controller 259,haptic feedback controller 261, and one or more input controllers 260for other input or control devices. The one or more input controllers260 receive/send electrical signals from/to other input control devices216. The other input control devices 216 optionally include physicalbuttons (e.g., push buttons, rocker buttons, etc.), dials, sliderswitches, joysticks, click wheels, and so forth. In some alternateembodiments, input controller(s) 260 are, optionally, coupled to any (ornone) of the following: a keyboard, an infrared port, a USB port, and apointer device such as a mouse. The one or more buttons (e.g., 308, FIG.3) optionally include an up/down button for volume control of speaker211 and/or microphone 213. The one or more buttons optionally include apush button (e.g., 306, FIG. 3).

A quick press of the push button disengages a lock of touch screen 212or begin a process that uses gestures on the touch screen to unlock thedevice, as described in U.S. patent application Ser. No. 11/322,549,“Unlocking a Device by Performing Gestures on an Unlock Image,” filedDec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated byreference in its entirety. A longer press of the push button (e.g., 306)turns power to device 200 on or off. The user is able to customize afunctionality of one or more of the buttons. Touch screen 212 is used toimplement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 212 provides an input interface and an outputinterface between the device and a user. Display controller 256 receivesand/or sends electrical signals from/to touch screen 212. Touch screen212 displays visual output to the user. The visual output includesgraphics, text, icons, video, and any combination thereof (collectivelytermed “graphics”). In some embodiments, some or all of the visualoutput correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set ofsensors that accepts input from the user based on haptic and/or tactilecontact. Touch screen 212 and display controller 256 (along with anyassociated modules and/or sets of instructions in memory 202) detectcontact (and any movement or breaking of the contact) on touch screen212 and convert the detected contact into interaction withuser-interface objects (e.g., one or more soft keys, icons, web pages,or images) that are displayed on touch screen 212. In an exemplaryembodiment, a point of contact between touch screen 212 and the usercorresponds to a finger of the user.

Touch screen 212 uses LCD (liquid crystal display) technology, LPD(light emitting polymer display) technology, or LED (light emittingdiode) technology, although other display technologies may be used inother embodiments. Touch screen 212 and display controller 256 detectcontact and any movement or breaking thereof using any of a plurality oftouch sensing technologies now known or later developed, including butnot limited to capacitive, resistive, infrared, and surface acousticwave technologies, as well as other proximity sensor arrays or otherelements for determining one or more points of contact with touch screen212. In an exemplary embodiment, projected mutual capacitance sensingtechnology is used, such as that found in the iPhone® and iPod Touch®from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 isanalogous to the multi-touch sensitive touchpads described in thefollowing U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No.6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932(Westerman), and/or U.S. Patent Publication 2002/0015024A1, each ofwhich is hereby incorporated by reference in its entirety. However,touch screen 212 displays visual output from device 200, whereastouch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 212 is asdescribed in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 212 has, for example, a video resolution in excess of 100dpi. In some embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user makes contact with touch screen 212using any suitable object or appendage, such as a stylus, a finger, andso forth. In some embodiments, the user interface is designed to workprimarily with finger-based contacts and gestures, which can be lessprecise than stylus-based input due to the larger area of contact of afinger on the touch screen. In some embodiments, the device translatesthe rough finger-based input into a precise pointer/cursor position orcommand for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200includes a touchpad (not shown) for activating or deactivatingparticular functions. In some embodiments, the touchpad is atouch-sensitive area of the device that, unlike the touch screen, doesnot display visual output. The touchpad is a touch-sensitive surfacethat is separate from touch screen 212 or an extension of thetouch-sensitive surface formed by the touch screen.

Device 200 also includes power system 262 for powering the variouscomponents. Power system 262 includes a power management system, one ormore power sources (e.g., battery, alternating current (AC)), arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 200 also includes one or more optical sensors 264. FIG. 2A showsan optical sensor coupled to optical sensor controller 258 in I/Osubsystem 206. Optical sensor 264 includes charge-coupled device (CCD)or complementary metal-oxide semiconductor (CMOS) phototransistors.Optical sensor 264 receives light from the environment, projectedthrough one or more lenses, and converts the light to data representingan image. In conjunction with imaging module 243 (also called a cameramodule), optical sensor 264 captures still images or video. In someembodiments, an optical sensor is located on the back of device 200,opposite touch screen display 212 on the front of the device so that thetouch screen display is used as a viewfinder for still and/or videoimage acquisition. In some embodiments, an optical sensor is located onthe front of the device so that the user's image is obtained for videoconferencing while the user views the other video conferenceparticipants on the touch screen display. In some embodiments, theposition of optical sensor 264 can be changed by the user (e.g., byrotating the lens and the sensor in the device housing) so that a singleoptical sensor 264 is used along with the touch screen display for bothvideo conferencing and still and/or video image acquisition.

Device 200 optionally also includes one or more contact intensitysensors 265. FIG. 2A shows a contact intensity sensor coupled tointensity sensor controller 259 in I/O subsystem 206. Contact intensitysensor 265 optionally includes one or more piezoresistive strain gauges,capacitive force sensors, electric force sensors, piezoelectric forcesensors, optical force sensors, capacitive touch-sensitive surfaces, orother intensity sensors (e.g., sensors used to measure the force (orpressure) of a contact on a touch-sensitive surface). Contact intensitysensor 265 receives contact intensity information (e.g., pressureinformation or a proxy for pressure information) from the environment.In some embodiments, at least one contact intensity sensor is collocatedwith, or proximate to, a touch-sensitive surface (e.g., touch-sensitivedisplay system 212). In some embodiments, at least one contact intensitysensor is located on the back of device 200, opposite touch screendisplay 212, which is located on the front of device 200.

Device 200 also includes one or more proximity sensors 266. FIG. 2Ashows proximity sensor 266 coupled to peripherals interface 218.Alternately, proximity sensor 266 is coupled to input controller 260 inI/O subsystem 206. Proximity sensor 266 is performed as described inU.S. patent application Ser. No. 11/241,839, “Proximity Detector InHandheld Device”; Ser. No. 11/240,788, “Proximity Detector In HandheldDevice”; Ser. No. 11/620,702, “Using Ambient Light Sensor To AugmentProximity Sensor Output”; Ser. No. 11/586,862, “Automated Response ToAnd Sensing Of User Activity In Portable Devices”; and Ser. No.11/638,251, “Methods And Systems For Automatic Configuration OfPeripherals,” which are hereby incorporated by reference in theirentirety. In some embodiments, the proximity sensor turns off anddisables touch screen 212 when the multifunction device is placed nearthe user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile outputgenerators 267. FIG. 2A shows a tactile output generator coupled tohaptic feedback controller 261 in I/O subsystem 206. Tactile outputgenerator 267 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 265 receives tactile feedbackgeneration instructions from haptic feedback module 233 and generatestactile outputs on device 200 that are capable of being sensed by a userof device 200. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 212) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 200) or laterally (e.g., back and forth inthe same plane as a surface of device 200). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 200, opposite touch screen display 212, which is located on thefront of device 200.

Device 200 also includes one or more accelerometers 268. FIG. 2A showsaccelerometer 268 coupled to peripherals interface 218. Alternately,accelerometer 268 is coupled to an input controller 260 in I/O subsystem206. Accelerometer 268 performs, for example, as described in U.S.Patent Publication No. 20050190059, “Acceleration-based Theft DetectionSystem for Portable Electronic Devices,” and U.S. Patent Publication No.20060017692, “Methods And Apparatuses For Operating A Portable DeviceBased On An Accelerometer,” both of which are incorporated by referenceherein in their entirety. In some embodiments, information is displayedon the touch screen display in a portrait view or a landscape view basedon an analysis of data received from the one or more accelerometers.Device 200 optionally includes, in addition to accelerometer(s) 268, amagnetometer (not shown) and a GPS (or GLONASS or other globalnavigation system) receiver (not shown) for obtaining informationconcerning the location and orientation (e.g., portrait or landscape) ofdevice 200.

In some embodiments, the software components stored in memory 202include operating system 226, communication module (or set ofinstructions) 228, contact/motion module (or set of instructions) 230,graphics module (or set of instructions) 232, text input module (or setof instructions) 234, Global Positioning System (GPS) module (or set ofinstructions) 235, Digital Assistant Client Module 229, and applications(or sets of instructions) 236. Further, memory 202 stores data andmodels, such as user data and models 231. Furthermore, in someembodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/globalinternal state 257, as shown in FIGS. 2A and 4. Device/global internalstate 257 includes one or more of: active application state, indicatingwhich applications, if any, are currently active; display state,indicating what applications, views or other information occupy variousregions of touch screen display 212; sensor state, including informationobtained from the device's various sensors and input control devices216; and location information concerning the device's location and/orattitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 228 facilitates communication with other devicesover one or more external ports 224 and also includes various softwarecomponents for handling data received by RF circuitry 208 and/orexternal port 224. External port 224 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin (e.g., 30-pin)connector that is the same as, or similar to and/or compatible with, the30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen212 (in conjunction with display controller 256) and othertouch-sensitive devices (e.g., a touchpad or physical click wheel).Contact/motion module 230 includes various software components forperforming various operations related to detection of contact, such asdetermining if contact has occurred (e.g., detecting a finger-downevent), determining an intensity of the contact (e.g., the force orpressure of the contact or a substitute for the force or pressure of thecontact), determining if there is movement of the contact and trackingthe movement across the touch-sensitive surface (e.g., detecting one ormore finger-dragging events), and determining if the contact has ceased(e.g., detecting a finger-up event or a break in contact).Contact/motion module 230 receives contact data from the touch-sensitivesurface. Determining movement of the point of contact, which isrepresented by a series of contact data, optionally includes determiningspeed (magnitude), velocity (magnitude and direction), and/or anacceleration (a change in magnitude and/or direction) of the point ofcontact. These operations are, optionally, applied to single contacts(e.g., one finger contacts) or to multiple simultaneous contacts (e.g.,“multitouch”/multiple finger contacts). In some embodiments,contact/motion module 230 and display controller 256 detect contact on atouchpad.

In some embodiments, contact/motion module 230 uses a set of one or moreintensity thresholds to determine whether an operation has beenperformed by a user (e.g., to determine whether a user has “clicked” onan icon). In some embodiments, at least a subset of the intensitythresholds are determined in accordance with software parameters (e.g.,the intensity thresholds are not determined by the activation thresholdsof particular physical actuators and can be adjusted without changingthe physical hardware of device 200). For example, a mouse “click”threshold of a trackpad or touch screen display can be set to any of alarge range of predefined threshold values without changing the trackpador touch screen display hardware. Additionally, in some implementations,a user of the device is provided with software settings for adjustingone or more of the set of intensity thresholds (e.g., by adjustingindividual intensity thresholds and/or by adjusting a plurality ofintensity thresholds at once with a system-level click “intensity”parameter).

Contact/motion module 230 optionally detects a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns (e.g., different motions, timings, and/or intensities ofdetected contacts). Thus, a gesture is, optionally, detected bydetecting a particular contact pattern. For example, detecting a fingertap gesture includes detecting a finger-down event followed by detectinga finger-up (liftoff) event at the same position (or substantially thesame position) as the finger-down event (e.g., at the position of anicon). As another example, detecting a finger swipe gesture on thetouch-sensitive surface includes detecting a finger-down event followedby detecting one or more finger-dragging events, and subsequentlyfollowed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components forrendering and displaying graphics on touch screen 212 or other display,including components for changing the visual impact (e.g., brightness,transparency, saturation, contrast, or other visual property) ofgraphics that are displayed. As used herein, the term “graphics”includes any object that can be displayed to a user, including, withoutlimitation, text, web pages, icons (such as user-interface objectsincluding soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representinggraphics to be used. Each graphic is, optionally, assigned acorresponding code. Graphics module 232 receives, from applicationsetc., one or more codes specifying graphics to be displayed along with,if necessary, coordinate data and other graphic property data, and thengenerates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components forgenerating instructions used by tactile output generator(s) 267 toproduce tactile outputs at one or more locations on device 200 inresponse to user interactions with device 200.

Text input module 234, which is, in some examples, a component ofgraphics module 232, provides soft keyboards for entering text invarious applications (e.g., contacts 237, email 240, IM 241, browser247, and any other application that needs text input).

GPS module 235 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 238 foruse in location-based dialing; to camera 243 as picture/video metadata;and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 includes various client-side digitalassistant instructions to provide the client-side functionalities of thedigital assistant. For example, digital assistant client module 229 iscapable of accepting voice input (e.g., speech input), text input, touchinput, and/or gestural input through various user interfaces (e.g.,microphone 213, accelerometer(s) 268, touch-sensitive display system212, optical sensor(s) 229, other input control devices 216, etc.) ofportable multifunction device 200. Digital assistant client module 229is also capable of providing output in audio (e.g., speech output),visual, and/or tactile forms through various output interfaces (e.g.,speaker 211, touch-sensitive display system 212, tactile outputgenerator(s) 267, etc.) of portable multifunction device 200. Forexample, output is provided as voice, sound, alerts, text messages,menus, graphics, videos, animations, vibrations, and/or combinations oftwo or more of the above. During operation, digital assistant clientmodule 229 communicates with DA server 106 using RF circuitry 208.

User data and models 231 include various data associated with the user(e.g., user-specific vocabulary data, user preference data,user-specified name pronunciations, data from the user's electronicaddress book, to-do lists, shopping lists, etc.) to provide theclient-side functionalities of the digital assistant. Further, user dataand models 231 include various models (e.g., speech recognition models,statistical language models, natural language processing models,ontology, task flow models, service models, etc.) for processing userinput and determining user intent.

In some examples, digital assistant client module 229 utilizes thevarious sensors, subsystems, and peripheral devices of portablemultifunction device 200 to gather additional information from thesurrounding environment of the portable multifunction device 200 toestablish a context associated with a user, the current userinteraction, and/or the current user input. In some examples, digitalassistant client module 229 provides the contextual information or asubset thereof with the user input to DA server 106 to help infer theuser's intent. In some examples, the digital assistant also uses thecontextual information to determine how to prepare and deliver outputsto the user. Contextual information is referred to as context data.

In some examples, the contextual information that accompanies the userinput includes sensor information, e.g., lighting, ambient noise,ambient temperature, images or videos of the surrounding environment,etc. In some examples, the contextual information can also include thephysical state of the device, e.g., device orientation, device location,device temperature, power level, speed, acceleration, motion patterns,cellular signals strength, etc. In some examples, information related tothe software state of DA server 106, e.g., running processes, installedprograms, past and present network activities, background services,error logs, resources usage, etc., and of portable multifunction device200 is provided to DA server 106 as contextual information associatedwith a user input.

In some examples, the digital assistant client module 229 selectivelyprovides information (e.g., user data 231) stored on the portablemultifunction device 200 in response to requests from DA server 106. Insome examples, digital assistant client module 229 also elicitsadditional input from the user via a natural language dialogue or otheruser interfaces upon request by DA server 106. Digital assistant clientmodule 229 passes the additional input to DA server 106 to help DAserver 106 in intent deduction and/or fulfillment of the user's intentexpressed in the user request.

A more detailed description of a digital assistant is described belowwith reference to FIGS. 7A-7C. It should be recognized that digitalassistant client module 229 can include any number of the sub-modules ofdigital assistant module 726 described below.

Applications 236 include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact        list);    -   Telephone module 238;    -   Video conference module 239;    -   E-mail client module 240;    -   Instant messaging (IM) module 241;    -   Workout support module 242;    -   Camera module 243 for still and/or video images;    -   Image management module 244;    -   Video player module;    -   Music player module;    -   Browser module 247;    -   Calendar module 248;    -   Widget modules 249, which includes, in some examples, one or        more of: weather widget 249-1, stocks widget 249-2, calculator        widget 249-3, alarm clock widget 249-4, dictionary widget 249-5,        and other widgets obtained by the user, as well as user-created        widgets 249-6;    -   Widget creator module 250 for making user-created widgets 249-6;    -   Search module 251;    -   Video and music player module 252, which merges video player        module and music player module;    -   Notes module 253;    -   Map module 254; and/or    -   Online video module 255.

Examples of other applications 236 that are stored in memory 202 includeother word processing applications, other image editing applications,drawing applications, presentation applications, JAVA-enabledapplications, encryption, digital rights management, voice recognition,and voice replication.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, contacts module 237 are used to manage an address book or contactlist (e.g., stored in application internal state 292 of contacts module237 in memory 202 or memory 470), including: adding name(s) to theaddress book; deleting name(s) from the address book; associatingtelephone number(s), e-mail address(es), physical address(es) or otherinformation with a name; associating an image with a name; categorizingand sorting names; providing telephone numbers or e-mail addresses toinitiate and/or facilitate communications by telephone 238, videoconference module 239, e-mail 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, contact/motionmodule 230, graphics module 232, and text input module 234, telephonemodule 238 are used to enter a sequence of characters corresponding to atelephone number, access one or more telephone numbers in contactsmodule 237, modify a telephone number that has been entered, dial arespective telephone number, conduct a conversation, and disconnect orhang up when the conversation is completed. As noted above, the wirelesscommunication uses any of a plurality of communications standards,protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, optical sensor264, optical sensor controller 258, contact/motion module 230, graphicsmodule 232, text input module 234, contacts module 237, and telephonemodule 238, video conference module 239 includes executable instructionsto initiate, conduct, and terminate a video conference between a userand one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, e-mail client module 240 includes executableinstructions to create, send, receive, and manage e-mail in response touser instructions. In conjunction with image management module 244,e-mail client module 240 makes it very easy to create and send e-mailswith still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, the instant messaging module 241 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages, and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages include graphics, photos, audio files, video filesand/or other attachments as are supported in an MMS and/or an EnhancedMessaging Service (EMS). As used herein, “instant messaging” refers toboth telephony-based messages (e.g., messages sent using SMS or MMS) andInternet-based messages (e.g., messages sent using XMPP, SIMPLE, orIMPS).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, map module 254, and music playermodule, workout support module 242 includes executable instructions tocreate workouts (e.g., with time, distance, and/or calorie burninggoals); communicate with workout sensors (sports devices); receiveworkout sensor data; calibrate sensors used to monitor a workout; selectand play music for a workout; and display, store, and transmit workoutdata.

In conjunction with touch screen 212, display controller 256, opticalsensor(s) 264, optical sensor controller 258, contact/motion module 230,graphics module 232, and image management module 244, camera module 243includes executable instructions to capture still images or video(including a video stream) and store them into memory 202, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 202.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, text input module 234,and camera module 243, image management module 244 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, browser module 247 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web pages or portionsthereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, e-mail client module 240, and browser module 247,calendar module 248 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to-do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, widget modules 249 aremini-applications that can be downloaded and used by a user (e.g.,weather widget 249-1, stocks widget 249-2, calculator widget 249-3,alarm clock widget 249-4, and dictionary widget 249-5) or created by theuser (e.g., user-created widget 249-6). In some embodiments, a widgetincludes an HTML (Hypertext Markup Language) file, a CSS (CascadingStyle Sheets) file, and a JavaScript file. In some embodiments, a widgetincludes an XML (Extensible Markup Language) file and a JavaScript file(e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, the widget creator module 250are used by a user to create widgets (e.g., turning a user-specifiedportion of a web page into a widget).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, search module 251 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 202 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, and browser module 247, video and musicplayer module 252 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 212 or on an external, connected display via externalport 224). In some embodiments, device 200 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, notes module 253 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, and browser module 247, map module 254are used to receive, display, modify, and store maps and data associatedwith maps (e.g., driving directions, data on stores and other points ofinterest at or near a particular location, and other location-baseddata) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, text input module 234, e-mail clientmodule 240, and browser module 247, online video module 255 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 224), send an e-mailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 241, rather than e-mail client module 240, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs, procedures, or modules,and thus various subsets of these modules can be combined or otherwiserearranged in various embodiments. For example, video player module canbe combined with music player module into a single module (e.g., videoand music player module 252, FIG. 2A). In some embodiments, memory 202stores a subset of the modules and data structures identified above.Furthermore, memory 202 stores additional modules and data structuresnot described above.

In some embodiments, device 200 is a device where operation ofapredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device200, the number of physical input control devices (such as push buttons,dials, and the like) on device 200 is reduced.

The predefined set of functions that are performed exclusively through atouch screen and/or a touchpad optionally include navigation betweenuser interfaces. In some embodiments, the touchpad, when touched by theuser, navigates device 200 to a main, home, or root menu from any userinterface that is displayed on device 200. In such embodiments, a “menubutton” is implemented using a touchpad. In some other embodiments, themenu button is a physical push button or other physical input controldevice instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., inoperating system 226) and a respective application 236-1 (e.g., any ofthe aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines theapplication 236-1 and application view 291 of application 236-1 to whichto deliver the event information. Event sorter 270 includes eventmonitor 271 and event dispatcher module 274. In some embodiments,application 236-1 includes application internal state 292, whichindicates the current application view(s) displayed on touch-sensitivedisplay 212 when the application is active or executing. In someembodiments, device/global internal state 257 is used by event sorter270 to determine which application(s) is (are) currently active, andapplication internal state 292 is used by event sorter 270 to determineapplication views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additionalinformation, such as one or more of: resume information to be used whenapplication 236-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 236-1, a state queue for enabling the user to go back toa prior state or view of application 236-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 271 receives event information from peripherals interface218. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 212, as part of a multi-touchgesture). Peripherals interface 218 transmits information it receivesfrom I/O subsystem 206 or a sensor, such as proximity sensor 266,accelerometer(s) 268, and/or microphone 213 (through audio circuitry210). Information that peripherals interface 218 receives from I/Osubsystem 206 includes information from touch-sensitive display 212 or atouch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripheralsinterface 218 at predetermined intervals. In response, peripheralsinterface 218 transmits event information. In other embodiments,peripherals interface 218 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit viewdetermination module 272 and/or an active event recognizer determinationmodule 273.

Hit view determination module 272 provides software procedures fordetermining where a sub-event has taken place within one or more viewswhen touch-sensitive display 212 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected correspond to programmatic levels within aprogrammatic or view hierarchy of the application. For example, thelowest level view in which a touch is detected is called the hit view,and the set of events that are recognized as proper inputs is determinedbased, at least in part, on the hit view of the initial touch thatbegins a touch-based gesture.

Hit view determination module 272 receives information related to subevents of a touch-based gesture. When an application has multiple viewsorganized in a hierarchy, hit view determination module 272 identifies ahit view as the lowest view in the hierarchy which should handle thesub-event. In most circumstances, the hit view is the lowest level viewin which an initiating sub-event occurs (e.g., the first sub-event inthe sequence of sub-events that form an event or potential event). Oncethe hit view is identified by the hit view determination module 272, thehit view typically receives all sub-events related to the same touch orinput source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 273 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 273 determines that all views that include thephysical location of a sub-event are actively involved views, andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 274 dispatches the event information to an eventrecognizer (e.g., event recognizer 280). In embodiments including activeevent recognizer determination module 273, event dispatcher module 274delivers the event information to an event recognizer determined byactive event recognizer determination module 273. In some embodiments,event dispatcher module 274 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270.Alternatively, application 236-1 includes event sorter 270. In yet otherembodiments, event sorter 270 is a stand-alone module, or a part ofanother module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of eventhandlers 290 and one or more application views 291, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 291 of the application 236-1 includes one or more event recognizers280. Typically, a respective application view 291 includes a pluralityof event recognizers 280. In other embodiments, one or more of eventrecognizers 280 are part of a separate module, such as a user interfacekit (not shown) or a higher level object from which application 236-1inherits methods and other properties. In some embodiments, a respectiveevent handler 290 includes one or more of: data updater 276, objectupdater 277, GUI updater 278, and/or event data 279 received from eventsorter 270. Event handler 290 utilizes or calls data updater 276, objectupdater 277, or GUI updater 278 to update the application internal state292. Alternatively, one or more of the application views 291 include oneor more respective event handlers 290. Also, in some embodiments, one ormore of data updater 276, object updater 277, and GUI updater 278 areincluded in a respective application view 291.

A respective event recognizer 280 receives event information (e.g.,event data 279) from event sorter 270 and identifies an event from theevent information. Event recognizer 280 includes event receiver 282 andevent comparator 284. In some embodiments, event recognizer 280 alsoincludes at least a subset of: metadata 283, and event deliveryinstructions 288 (which include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch, the eventinformation also includes speed and direction of the sub-event. In someembodiments, events include rotation of the device from one orientationto another (e.g., from a portrait orientation to a landscapeorientation, or vice versa), and the event information includescorresponding information about the current orientation (also calleddevice attitude) of the device.

Event comparator 284 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 284 includes eventdefinitions 286. Event definitions 286 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(287-1), event 2 (287-2), and others. In some embodiments, sub-events inan event (287) include, for example, touch begin, touch end, touchmovement, touch cancellation, and multiple touching. In one example, thedefinition for event 1 (287-1) is a double tap on a displayed object.The double tap, for example, comprises a first touch (touch begin) onthe displayed object for a predetermined phase, a first liftoff (touchend) for a predetermined phase, a second touch (touch begin) on thedisplayed object for a predetermined phase, and a second liftoff (touchend) for a predetermined phase. In another example, the definition forevent 2 (287-2) is a dragging on a displayed object. The dragging, forexample, comprises a touch (or contact) on the displayed object for apredetermined phase, a movement of the touch across touch-sensitivedisplay 212, and liftoff of the touch (touch end). In some embodiments,the event also includes information for one or more associated eventhandlers 290.

In some embodiments, event definition 287 includes a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 284 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 212, when a touch is detected on touch-sensitivedisplay 212, event comparator 284 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 290, the event comparator uses the result of the hit testto determine which event handler 290 should be activated. For example,event comparator 284 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series ofsub-events do not match any of the events in event definitions 286, therespective event recognizer 280 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata283 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 283 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers interact, or are enabled to interact, with one another. Insome embodiments, metadata 283 includes configurable properties, flags,and/or lists that indicate whether sub-events are delivered to varyinglevels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates eventhandler 290 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 280 delivers event information associated with theevent to event handler 290. Activating an event handler 290 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 280 throws a flag associated withthe recognized event, and event handler 290 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used inapplication 236-1. For example, data updater 276 updates the telephonenumber used in contacts module 237, or stores a video file used in videoplayer module. In some embodiments, object updater 277 creates andupdates objects used in application 236-1. For example, object updater277 creates a new user-interface object or updates the position of auser-interface object. GUI updater 278 updates the GUI. For example, GUIupdater 278 prepares display information and sends it to graphics module232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to dataupdater 276, object updater 277, and GUI updater 278. In someembodiments, data updater 276, object updater 277, and GUI updater 278are included in a single module of a respective application 236-1 orapplication view 291. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 200 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 3 illustrates a portable multifunction device 200 having a touchscreen 212 in accordance with some embodiments. The touch screenoptionally displays one or more graphics within user interface (UI) 300.In this embodiment, as well as others described below, a user is enabledto select one or more of the graphics by making a gesture on thegraphics, for example, with one or more fingers 302 (not drawn to scalein the figure) or one or more styluses 303 (not drawn to scale in thefigure). In some embodiments, selection of one or more graphics occurswhen the user breaks contact with the one or more graphics. In someembodiments, the gesture optionally includes one or more taps, one ormore swipes (from left to right, right to left, upward and/or downward),and/or a rolling of a finger (from right to left, left to right, upwardand/or downward) that has made contact with device 200. In someimplementations or circumstances, inadvertent contact with a graphicdoes not select the graphic. For example, a swipe gesture that sweepsover an application icon optionally does not select the correspondingapplication when the gesture corresponding to selection is a tap.

Device 200 also includes one or more physical buttons, such as “home” ormenu button 304. As described previously, menu button 304 is used tonavigate to any application 236 in a set of applications that isexecuted on device 200. Alternatively, in some embodiments, the menubutton is implemented as a soft key in a GUI displayed on touch screen212.

In one embodiment, device 200 includes touch screen 212, menu button304, push button 306 for powering the device on/off and locking thedevice, volume adjustment button(s) 308, subscriber identity module(SIM) card slot 310, headset jack 312, and docking/charging externalport 224. Push button 306 is, optionally, used to turn the power on/offon the device by depressing the button and holding the button in thedepressed state for a predefined time interval; to lock the device bydepressing the button and releasing the button before the predefinedtime interval has elapsed; and/or to unlock the device or initiate anunlock process. In an alternative embodiment, device 200 also acceptsverbal input for activation or deactivation of some functions throughmicrophone 213. Device 200 also, optionally, includes one or morecontact intensity sensors 265 for detecting intensity of contacts ontouch screen 212 and/or one or more tactile output generators 267 forgenerating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 400 need not be portable. In some embodiments,device 400 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 400 typically includesone or more processing units (CPUs) 410, one or more network or othercommunications interfaces 460, memory 470, and one or more communicationbuses 420 for interconnecting these components. Communication buses 420optionally include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Device 400 includes input/output (I/O) interface 430 comprising display440, which is typically a touch screen display. I/O interface 430 alsooptionally includes a keyboard and/or mouse (or other pointing device)450 and touchpad 455, tactile output generator 457 for generatingtactile outputs on device 400 (e.g., similar to tactile outputgenerator(s) 267 described above with reference to FIG. 2A), sensors 459(e.g., optical, acceleration, proximity, touch-sensitive, and/or contactintensity sensors similar to contact intensity sensor(s) 265 describedabove with reference to FIG. 2A). Memory 470 includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, or other random access solidstate memory devices; and optionally includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid state storagedevices. Memory 470 optionally includes one or more storage devicesremotely located from CPU(s) 410. In some embodiments, memory 470 storesprograms, modules, and data structures analogous to the programs,modules, and data structures stored in memory 202 of portablemultifunction device 200 (FIG. 2A), or a subset thereof. Furthermore,memory 470 optionally stores additional programs, modules, and datastructures not present in memory 202 of portable multifunction device200. For example, memory 470 of device 400 optionally stores drawingmodule 480, presentation module 482, word processing module 484, websitecreation module 486, disk authoring module 488, and/or spreadsheetmodule 490, while memory 202 of portable multifunction device 200 (FIG.2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 is, in some examples,stored in one or more of the previously mentioned memory devices. Eachof the above-identified modules corresponds to a set of instructions forperforming a function described above. The above-identified modules orprograms (e.g., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these modules are combined or otherwise rearranged in variousembodiments. In some embodiments, memory 470 stores a subset of themodules and data structures identified above. Furthermore, memory 470stores additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces thatcan be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on portable multifunction device 200 in accordance withsome embodiments. Similar user interfaces are implemented on device 400.In some embodiments, user interface 500 includes the following elements,or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such ascellular and Wi-Fi signals;

-   -   Time 504;    -   Bluetooth indicator 505;    -   Battery status indicator 506;    -   Tray 508 with icons for frequently used applications, such as:        -   Icon 516 for telephone module 238, labeled “Phone,” which            optionally includes an indicator 514 of the number of missed            calls or voicemail messages;        -   Icon 518 for e-mail client module 240, labeled “Mail,” which            optionally includes an indicator 510 of the number of unread            e-mails;        -   Icon 520 for browser module 247, labeled “Browser;” and        -   Icon 522 for video and music player module 252, also            referred to as iPod (trademark of Apple Inc.) module 252,            labeled “iPod;” and    -   Icons for other applications, such as:        -   Icon 524 for IM module 241, labeled “Messages;”        -   Icon 526 for calendar module 248, labeled “Calendar;”        -   Icon 528 for image management module 244, labeled “Photos;”        -   Icon 530 for camera module 243, labeled “Camera;”        -   Icon 532 for online video module 255, labeled “Online            Video;”        -   Icon 534 for stocks widget 249-2, labeled “Stocks;”        -   Icon 536 for map module 254, labeled “Maps;”        -   Icon 538 for weather widget 249-1, labeled “Weather;”        -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”        -   Icon 542 for workout support module 242, labeled “Workout            Support;”        -   Icon 544 for notes module 253, labeled “Notes;” and        -   Icon 546 for a settings application or module, labeled            “Settings,” which provides access to settings for device 200            and its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A aremerely exemplary. For example, icon 522 for video and music playermodule 252 is optionally labeled “Music” or “Music Player.” Other labelsare, optionally, used for various application icons. In someembodiments, a label for a respective application icon includes a nameof an application corresponding to the respective application icon. Insome embodiments, a label for a particular application icon is distinctfrom a name of an application corresponding to the particularapplication icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g.,device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tabletor touchpad 455, FIG. 4) that is separate from the display 550 (e.g.,touch screen display 212). Device 400 also, optionally, includes one ormore contact intensity sensors (e.g., one or more of sensors 457) fordetecting intensity of contacts on touch-sensitive surface 551 and/orone or more tactile output generators 459 for generating tactile outputsfor a user of device 400.

Although some of the examples which follow will be given with referenceto inputs on touch screen display 212 (where the touch-sensitive surfaceand the display are combined), in some embodiments, the device detectsinputs on a touch-sensitive surface that is separate from the display,as shown in FIG. 5B. In some embodiments, the touch-sensitive surface(e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) thatcorresponds to a primary axis (e.g., 553 in FIG. 5B) on the display(e.g., 550). In accordance with these embodiments, the device detectscontacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface551 at locations that correspond to respective locations on the display(e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570).In this way, user inputs (e.g., contacts 560 and 562, and movementsthereof) detected by the device on the touch-sensitive surface (e.g.,551 in FIG. 5B) are used by the device to manipulate the user interfaceon the display (e.g., 550 in FIG. 5B) of the multifunction device whenthe touch-sensitive surface is separate from the display. It should beunderstood that similar methods are, optionally, used for other userinterfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600includes body 602. In some embodiments, device 600 includes some or allof the features described with respect to devices 200 and 400 (e.g.,FIGS. 2A-4). In some embodiments, device 600 has touch-sensitive displayscreen 604, hereafter touch screen 604. Alternatively, or in addition totouch screen 604, device 600 has a display and a touch-sensitivesurface. As with devices 200 and 400, in some embodiments, touch screen604 (or the touch-sensitive surface) has one or more intensity sensorsfor detecting intensity of contacts (e.g., touches) being applied. Theone or more intensity sensors of touch screen 604 (or thetouch-sensitive surface) provide output data that represents theintensity of touches. The user interface of device 600 responds totouches based on their intensity, meaning that touches of differentintensities can invoke different user interface operations on device600.

Techniques for detecting and processing touch intensity are found, forexample, in related applications: International Patent ApplicationSerial No. PCT/US2013/040061, titled “Device, Method, and Graphical UserInterface for Displaying User Interface Objects Corresponding to anApplication,” filed May 8, 2013, and International Patent ApplicationSerial No. PCT/US2013/069483, titled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” filed Nov. 11, 2013, each of which is herebyincorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and608. Input mechanisms 606 and 608, if included, are physical. Examplesof physical input mechanisms include push buttons and rotatablemechanisms. In some embodiments, device 600 has one or more attachmentmechanisms. Such attachment mechanisms, if included, can permitattachment of device 600 with, for example, hats, eyewear, earrings,necklaces, shirts, jackets, bracelets, watch straps, chains, trousers,belts, shoes, purses, backpacks, and so forth. These attachmentmechanisms permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In someembodiments, device 600 includes some or all of the components describedwith respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612 thatoperatively couples I/O section 614 with one or more computer processors616 and memory 618. I/O section 614 is connected to display 604, whichcan have touch-sensitive component 622 and, optionally, touch-intensitysensitive component 624. In addition, I/O section 614 is connected withcommunication unit 630 for receiving application and operating systemdata, using Wi-Fi, Bluetooth, near field communication (NFC), cellular,and/or other wireless communication techniques. Device 600 includesinput mechanisms 606 and/or 608. Input mechanism 606 is a rotatableinput device or a depressible and rotatable input device, for example.Input mechanism 608 is a button, in some examples.

Input mechanism 608 is a microphone, in some examples. Personalelectronic device 600 includes, for example, various sensors, such asGPS sensor 632, accelerometer 634, directional sensor 640 (e.g.,compass), gyroscope 636, motion sensor 638, and/or a combinationthereof, all of which are operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 is a non-transitorycomputer-readable storage medium, for storing computer-executableinstructions, which, when executed by one or more computer processors616, for example, cause the computer processors to perform thetechniques and processes described below. The computer-executableinstructions, for example, are also stored and/or transported within anynon-transitory computer-readable storage medium for use by or inconnection with an instruction execution system, apparatus, or device,such as a computer-based system, processor-containing system, or othersystem that can fetch the instructions from the instruction executionsystem, apparatus, or device and execute the instructions. Personalelectronic device 600 is not limited to the components and configurationof FIG. 6B, but can include other or additional components in multipleconfigurations.

As used here, the term “affordance” refers to a user-interactivegraphical user interface object that is, for example, displayed on thedisplay screen of devices 200, 400, and/or 600 (FIGS. 2A-B, 4, and6A-B). For example, an image (e.g., icon), a button, and text (e.g.,hyperlink) each constitutes an affordance.

As used herein, the term “focus selector” refers to an input elementthat indicates a current part of a user interface with which a user isinteracting. In some implementations that include a cursor or otherlocation marker, the cursor acts as a “focus selector” so that when aninput (e.g., a press input) is detected on a touch-sensitive surface(e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B)while the cursor is over a particular user interface element (e.g., abutton, window, slider or other user interface element), the particularuser interface element is adjusted in accordance with the detectedinput. In some implementations that include a touch screen display(e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212in FIG. 5A) that enables direct interaction with user interface elementson the touch screen display, a detected contact on the touch screen actsas a “focus selector” so that when an input (e.g., a press input by thecontact) is detected on the touch screen display at a location of aparticular user interface element (e.g., a button, window, slider, orother user interface element), the particular user interface element isadjusted in accordance with the detected input. In some implementations,focus is moved from one region of a user interface to another region ofthe user interface without corresponding movement of a cursor ormovement of a contact on a touch screen display (e.g., by using a tabkey or arrow keys to move focus from one button to another button); inthese implementations, the focus selector moves in accordance withmovement of focus between different regions of the user interface.Without regard to the specific form taken by the focus selector, thefocus selector is generally the user interface element (or contact on atouch screen display) that is controlled by the user so as tocommunicate the user's intended interaction with the user interface(e.g., by indicating, to the device, the element of the user interfacewith which the user is intending to interact). For example, the locationof a focus selector (e.g., a cursor, a contact, or a selection box) overa respective button while a press input is detected on thetouch-sensitive surface (e.g., a touchpad or touch screen) will indicatethat the user is intending to activate the respective button (as opposedto other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristicintensity” of a contact refers to a characteristic of the contact basedon one or more intensities of the contact. In some embodiments, thecharacteristic intensity is based on multiple intensity samples. Thecharacteristic intensity is, optionally, based on a predefined number ofintensity samples, or a set of intensity samples collected during apredetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10seconds) relative to a predefined event (e.g., after detecting thecontact, prior to detecting liftoff of the contact, before or afterdetecting a start of movement of the contact, prior to detecting an endof the contact, before or after detecting an increase in intensity ofthe contact, and/or before or after detecting a decrease in intensity ofthe contact). A characteristic intensity of a contact is, optionallybased on one or more of: a maximum value of the intensities of thecontact, a mean value of the intensities of the contact, an averagevalue of the intensities of the contact, a top 10 percentile value ofthe intensities of the contact, a value at the half maximum of theintensities of the contact, a value at the 90 percent maximum of theintensities of the contact, or the like. In some embodiments, theduration of the contact is used in determining the characteristicintensity (e.g., when the characteristic intensity is an average of theintensity of the contact over time). In some embodiments, thecharacteristic intensity is compared to a set of one or more intensitythresholds to determine whether an operation has been performed by auser. For example, the set of one or more intensity thresholds includesa first intensity threshold and a second intensity threshold. In thisexample, a contact with a characteristic intensity that does not exceedthe first threshold results in a first operation, a contact with acharacteristic intensity that exceeds the first intensity threshold anddoes not exceed the second intensity threshold results in a secondoperation, and a contact with a characteristic intensity that exceedsthe second threshold results in a third operation. In some embodiments,a comparison between the characteristic intensity and one or morethresholds is used to determine whether or not to perform one or moreoperations (e.g., whether to perform a respective operation or forgoperforming the respective operation) rather than being used to determinewhether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposesof determining a characteristic intensity. For example, atouch-sensitive surface receives a continuous swipe contacttransitioning from a start location and reaching an end location, atwhich point the intensity of the contact increases. In this example, thecharacteristic intensity of the contact at the end location is based ononly a portion of the continuous swipe contact, and not the entire swipecontact (e.g., only the portion of the swipe contact at the endlocation). In some embodiments, a smoothing algorithm is applied to theintensities of the swipe contact prior to determining the characteristicintensity of the contact. For example, the smoothing algorithmoptionally includes one or more of: an unweighted sliding-averagesmoothing algorithm, a triangular smoothing algorithm, a median filtersmoothing algorithm, and/or an exponential smoothing algorithm. In somecircumstances, these smoothing algorithms eliminate narrow spikes ordips in the intensities of the swipe contact for purposes of determininga characteristic intensity.

The intensity of a contact on the touch-sensitive surface ischaracterized relative to one or more intensity thresholds, such as acontact-detection intensity threshold, a light press intensitythreshold, a deep press intensity threshold, and/or one or more otherintensity thresholds. In some embodiments, the light press intensitythreshold corresponds to an intensity at which the device will performoperations typically associated with clicking a button of a physicalmouse or a trackpad. In some embodiments, the deep press intensitythreshold corresponds to an intensity at which the device will performoperations that are different from operations typically associated withclicking a button of a physical mouse or a trackpad. In someembodiments, when a contact is detected with a characteristic intensitybelow the light press intensity threshold (e.g., and above a nominalcontact-detection intensity threshold below which the contact is nolonger detected), the device will move a focus selector in accordancewith movement of the contact on the touch-sensitive surface withoutperforming an operation associated with the light press intensitythreshold or the deep press intensity threshold. Generally, unlessotherwise stated, these intensity thresholds are consistent betweendifferent sets of user interface figures.

An increase of characteristic intensity of the contact from an intensitybelow the light press intensity threshold to an intensity between thelight press intensity threshold and the deep press intensity thresholdis sometimes referred to as a “light press” input. An increase ofcharacteristic intensity of the contact from an intensity below the deeppress intensity threshold to an intensity above the deep press intensitythreshold is sometimes referred to as a “deep press” input. An increaseof characteristic intensity of the contact from an intensity below thecontact-detection intensity threshold to an intensity between thecontact-detection intensity threshold and the light press intensitythreshold is sometimes referred to as detecting the contact on thetouch-surface. A decrease of characteristic intensity of the contactfrom an intensity above the contact-detection intensity threshold to anintensity below the contact-detection intensity threshold is sometimesreferred to as detecting liftoff of the contact from the touch-surface.In some embodiments, the contact-detection intensity threshold is zero.In some embodiments, the contact-detection intensity threshold isgreater than zero.

In some embodiments described herein, one or more operations areperformed in response to detecting a gesture that includes a respectivepress input or in response to detecting the respective press inputperformed with a respective contact (or a plurality of contacts), wherethe respective press input is detected based at least in part ondetecting an increase in intensity of the contact (or plurality ofcontacts) above a press-input intensity threshold. In some embodiments,the respective operation is performed in response to detecting theincrease in intensity of the respective contact above the press-inputintensity threshold (e.g., a “down stroke” of the respective pressinput). In some embodiments, the press input includes an increase inintensity of the respective contact above the press-input intensitythreshold and a subsequent decrease in intensity of the contact belowthe press-input intensity threshold, and the respective operation isperformed in response to detecting the subsequent decrease in intensityof the respective contact below the press-input threshold (e.g., an “upstroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoidaccidental inputs sometimes termed “jitter,” where the device defines orselects a hysteresis intensity threshold with a predefined relationshipto the press-input intensity threshold (e.g., the hysteresis intensitythreshold is X intensity units lower than the press-input intensitythreshold or the hysteresis intensity threshold is 75%, 90%, or somereasonable proportion of the press-input intensity threshold). Thus, insome embodiments, the press input includes an increase in intensity ofthe respective contact above the press-input intensity threshold and asubsequent decrease in intensity of the contact below the hysteresisintensity threshold that corresponds to the press-input intensitythreshold, and the respective operation is performed in response todetecting the subsequent decrease in intensity of the respective contactbelow the hysteresis intensity threshold (e.g., an “up stroke” of therespective press input). Similarly, in some embodiments, the press inputis detected only when the device detects an increase in intensity of thecontact from an intensity at or below the hysteresis intensity thresholdto an intensity at or above the press-input intensity threshold and,optionally, a subsequent decrease in intensity of the contact to anintensity at or below the hysteresis intensity, and the respectiveoperation is performed in response to detecting the press input (e.g.,the increase in intensity of the contact or the decrease in intensity ofthe contact, depending on the circumstances).

For ease of explanation, the descriptions of operations performed inresponse to a press input associated with a press-input intensitythreshold or in response to a gesture including the press input are,optionally, triggered in response to detecting either: an increase inintensity of a contact above the press-input intensity threshold, anincrease in intensity of a contact from an intensity below thehysteresis intensity threshold to an intensity above the press-inputintensity threshold, a decrease in intensity of the contact below thepress-input intensity threshold, and/or a decrease in intensity of thecontact below the hysteresis intensity threshold corresponding to thepress-input intensity threshold. Additionally, in examples where anoperation is described as being performed in response to detecting adecrease in intensity of a contact below the press-input intensitythreshold, the operation is, optionally, performed in response todetecting a decrease in intensity of the contact below a hysteresisintensity threshold corresponding to, and lower than, the press-inputintensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 inaccordance with various examples. In some examples, digital assistantsystem 700 is implemented on a standalone computer system. In someexamples, digital assistant system 700 is distributed across multiplecomputers. In some examples, some of the modules and functions of thedigital assistant are divided into a server portion and a clientportion, where the client portion resides on one or more user devices(e.g., devices 104, 122, 200, 400, or 600) and communicates with theserver portion (e.g., server system 108) through one or more networks,e.g., as shown in FIG. 1. In some examples, digital assistant system 700is an implementation of server system 108 (and/or DA server 106) shownin FIG. 1. It should be noted that digital assistant system 700 is onlyone example of a digital assistant system, and that digital assistantsystem 700 can have more or fewer components than shown, can combine twoor more components, or can have a different configuration or arrangementof the components. The various components shown in FIG. 7A areimplemented in hardware, software instructions for execution by one ormore processors, firmware, including one or more signal processingand/or application specific integrated circuits, or a combinationthereof.

Digital assistant system 700 includes memory 702, one or more processors704, input/output (I/O) interface 706, and network communicationsinterface 708. These components can communicate with one another overone or more communication buses or signal lines 710.

In some examples, memory 702 includes a non-transitory computer-readablemedium, such as high-speed random access memory and/or a non-volatilecomputer-readable storage medium (e.g., one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices).

In some examples, I/O interface 706 couples input/output devices 716 ofdigital assistant system 700, such as displays, keyboards, touchscreens, and microphones, to user interface module 722. I/O interface706, in conjunction with user interface module 722, receives user inputs(e.g., voice input, keyboard inputs, touch inputs, etc.) and processesthem accordingly. In some examples, e.g., when the digital assistant isimplemented on a standalone user device, digital assistant system 700includes any of the components and I/O communication interfacesdescribed with respect to devices 200, 400, or 600 in FIGS. 2A, 4, 6A-B,respectively. In some examples, digital assistant system 700 representsthe server portion of a digital assistant implementation, and caninteract with the user through a client-side portion residing on a userdevice (e.g., devices 104, 200, 400, or 600).

In some examples, the network communications interface 708 includeswired communication port(s) 712 and/or wireless transmission andreception circuitry 714. The wired communication port(s) receives andsend communication signals via one or more wired interfaces, e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wirelesscircuitry 714 receives and sends RF signals and/or optical signalsfrom/to communications networks and other communications devices. Thewireless communications use any of a plurality of communicationsstandards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA,Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communicationprotocol. Network communications interface 708 enables communicationbetween digital assistant system 700 with networks, such as theInternet, an intranet, and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN), and/or ametropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media ofmemory 702, stores programs, modules, instructions, and data structuresincluding all or a subset of: operating system 718, communicationsmodule 720, user interface module 722, one or more applications 724, anddigital assistant module 726. In particular, memory 702, or thecomputer-readable storage media of memory 702, stores instructions forperforming the processes described below. One or more processors 704execute these programs, modules, and instructions, and reads/writesfrom/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communications between varioushardware, firmware, and software components.

Communications module 720 facilitates communications between digitalassistant system 700 with other devices over network communicationsinterface 708. For example, communications module 720 communicates withRF circuitry 208 of electronic devices such as devices 200, 400, and 600shown in FIG. 2A, 4, 6A-B, respectively. Communications module 720 alsoincludes various components for handling data received by wirelesscircuitry 714 and/or wired communications port 712.

User interface module 722 receives commands and/or inputs from a uservia I/O interface 706 (e.g., from a keyboard, touch screen, pointingdevice, controller, and/or microphone), and generate user interfaceobjects on a display. User interface module 722 also prepares anddelivers outputs (e.g., speech, sound, animation, text, icons,vibrations, haptic feedback, light, etc.) to the user via the I/Ointerface 706 (e.g., through displays, audio channels, speakers,touch-pads, etc.).

Applications 724 include programs and/or modules that are configured tobe executed by one or more processors 704. For example, if the digitalassistant system is implemented on a standalone user device,applications 724 include user applications, such as games, a calendarapplication, a navigation application, or an email application. Ifdigital assistant system 700 is implemented on a server, applications724 include resource management applications, diagnostic applications,or scheduling applications, for example.

Memory 702 also stores digital assistant module 726 (or the serverportion of a digital assistant). In some examples, digital assistantmodule 726 includes the following sub-modules, or a subset or supersetthereof: input/output processing module 728, speech-to-text (STT)processing module 730, natural language processing module 732, dialogueflow processing module 734, task flow processing module 736, serviceprocessing module 738, and speech synthesis module 740. Each of thesemodules has access to one or more of the following systems or data andmodels of the digital assistant module 726, or a subset or supersetthereof: ontology 760, vocabulary index 744, user data 748, task flowmodels 754, service models 756, and ASR systems.

In some examples, using the processing modules, data, and modelsimplemented in digital assistant module 726, the digital assistant canperform at least some of the following: converting speech input intotext; identifying a user's intent expressed in a natural language inputreceived from the user; actively eliciting and obtaining informationneeded to fully infer the user's intent (e.g., by disambiguating words,games, intentions, etc.); determining the task flow for fulfilling theinferred intent; and executing the task flow to fulfill the inferredintent.

In some examples, as shown in FIG. 7B, I/O processing module 728interacts with the user through I/O devices 716 in FIG. 7A or with auser device (e.g., devices 104, 200, 400, or 600) through networkcommunications interface 708 in FIG. 7A to obtain user input (e.g., aspeech input) and to provide responses (e.g., as speech outputs) to theuser input. I/O processing module 728 optionally obtains contextualinformation associated with the user input from the user device, alongwith or shortly after the receipt of the user input. The contextualinformation includes user-specific data, vocabulary, and/or preferencesrelevant to the user input. In some examples, the contextual informationalso includes software and hardware states of the user device at thetime the user request is received, and/or information related to thesurrounding environment of the user at the time that the user requestwas received. In some examples, I/O processing module 728 also sendsfollow-up questions to, and receive answers from, the user regarding theuser request. When a user request is received by I/O processing module728 and the user request includes speech input, I/O processing module728 forwards the speech input to STT processing module 730 (or speechrecognizer) for speech-to-text conversions.

STT processing module 730 includes one or more ASR systems. The one ormore ASR systems can process the speech input that is received throughI/O processing module 728 to produce a recognition result. Each ASRsystem includes a front-end speech pre-processor. The front-end speechpre-processor extracts representative features from the speech input.For example, the front-end speech pre-processor performs a Fouriertransform on the speech input to extract spectral features thatcharacterize the speech input as a sequence of representativemulti-dimensional vectors. Further, each ASR system includes one or morespeech recognition models (e.g., acoustic models and/or language models)and implements one or more speech recognition engines. Examples ofspeech recognition models include Hidden Markov Models, Gaussian-MixtureModels, Deep Neural Network Models, n-gram language models, and otherstatistical models. Examples of speech recognition engines include thedynamic time warping based engines and weighted finite-state transducers(WFST) based engines. The one or more speech recognition models and theone or more speech recognition engines are used to process the extractedrepresentative features of the front-end speech pre-processor to produceintermediate recognitions results (e.g., phonemes, phonemic strings, andsub-words), and ultimately, text recognition results (e.g., words, wordstrings, or sequence of tokens). In some examples, the speech input isprocessed at least partially by a third-party service or on the user'sdevice (e.g., device 104, 200, 400, or 600) to produce the recognitionresult. Once STT processing module 730 produces recognition resultscontaining a text string (e.g., words, or sequence of words, or sequenceof tokens), the recognition result is passed to natural languageprocessing module 732 for intent deduction. In some examples, STTprocessing module 730 produces multiple candidate text representationsof the speech input. Each candidate text representation is a sequence ofwords or tokens corresponding to the speech input. In some examples,each candidate text representation is associated with a speechrecognition confidence score. Based on the speech recognition confidencescores, STT processing module 730 ranks the candidate textrepresentations and provides the n-best (e.g., n highest ranked)candidate text representation(s) to natural language processing module732 for intent deduction, where n is a predetermined integer greaterthan zero. For example, in one example, only the highest ranked (n=1)candidate text representation is passed to natural language processingmodule 732 for intent deduction. In another example, the five highestranked (n=5) candidate text representations are passed to naturallanguage processing module 732 for intent deduction.

More details on the speech-to-text processing are described in U.S.Utility application Ser. No. 13/236,942 for “Consolidating SpeechRecognition Results,” filed on Sep. 20, 2011, the entire disclosure ofwhich is incorporated herein by reference.

In some examples, STT processing module 730 includes and/or accesses avocabulary of recognizable words via phonetic alphabet conversion module731. Each vocabulary word is associated with one or more candidatepronunciations of the word represented in a speech recognition phoneticalphabet. In particular, the vocabulary of recognizable words includes aword that is associated with a plurality of candidate pronunciations.For example, the vocabulary includes the word “tomato” that isassociated with the candidate pronunciations of /

/ and /

/. Further, vocabulary words are associated with custom candidatepronunciations that are based on previous speech inputs from the user.Such custom candidate pronunciations are stored in STT processing module730 and are associated with a particular user via the user's profile onthe device. In some examples, the candidate pronunciations for words aredetermined based on the spelling of the word and one or more linguisticand/or phonetic rules. In some examples, the candidate pronunciationsare manually generated, e.g., based on known canonical pronunciations.

In some examples, the candidate pronunciations are ranked based on thecommonness of the candidate pronunciation. For example, the candidatepronunciation /

/ is ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., amongall users, for users in a particular geographical region, or for anyother appropriate subset of users). In some examples, candidatepronunciations are ranked based on whether the candidate pronunciationis a custom candidate pronunciation associated with the user. Forexample, custom candidate pronunciations are ranked higher thancanonical candidate pronunciations. This can be useful for recognizingproper nouns having a unique pronunciation that deviates from canonicalpronunciation. In some examples, candidate pronunciations are associatedwith one or more speech characteristics, such as geographic origin,nationality, or ethnicity. For example, the candidate pronunciation /

/ is associated with the United States, whereas the candidatepronunciation /

/ is associated with Great Britain. Further, the rank of the candidatepronunciation is based on one or more characteristics (e.g., geographicorigin, nationality, ethnicity, etc.) of the user stored in the user'sprofile on the device. For example, it can be determined from the user'sprofile that the user is associated with the United States. Based on theuser being associated with the United States, the candidatepronunciation /

/ (associated with the United States) is ranked higher than thecandidate pronunciation /

/ (associated with Great Britain). In some examples, one of the rankedcandidate pronunciations is selected as a predicted pronunciation (e.g.,the most likely pronunciation).

When a speech input is received, STT processing module 730 is used todetermine the phonemes corresponding to the speech input (e.g., using anacoustic model), and then attempt to determine words that match thephonemes (e.g., using a language model). For example, if STT processingmodule 730 first identifies the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine,based on vocabulary index 744, that this sequence corresponds to theword “tomato.”

In some examples, STT processing module 730 uses approximate matchingtechniques to determine words in an utterance. Thus, for example, theSTT processing module 730 determines that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence ofphonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) ofthe digital assistant takes the n-best candidate text representation(s)(“word sequence(s)” or “token sequence(s)”) generated by STT processingmodule 730, and attempts to associate each of the candidate textrepresentations with one or more “actionable intents” recognized by thedigital assistant. An “actionable intent” (or “user intent”) representsa task that can be performed by the digital assistant, and can have anassociated task flow implemented in task flow models 754. The associatedtask flow is a series of programmed actions and steps that the digitalassistant takes in order to perform the task. The scope of a digitalassistant's capabilities is dependent on the number and variety of taskflows that have been implemented and stored in task flow models 754, orin other words, on the number and variety of “actionable intents” thatthe digital assistant recognizes. The effectiveness of the digitalassistant, however, also dependents on the assistant's ability to inferthe correct “actionable intent(s)” from the user request expressed innatural language.

In some examples, in addition to the sequence of words or tokensobtained from STT processing module 730, natural language processingmodule 732 also receives contextual information associated with the userrequest, e.g., from I/O processing module 728. The natural languageprocessing module 732 optionally uses the contextual information toclarify, supplement, and/or further define the information contained inthe candidate text representations received from STT processing module730. The contextual information includes, for example, user preferences,hardware, and/or software states of the user device, sensor informationcollected before, during, or shortly after the user request, priorinteractions (e.g., dialogue) between the digital assistant and theuser, and the like. As described herein, contextual information is, insome examples, dynamic, and changes with time, location, content of thedialogue, and other factors.

In some examples, the natural language processing is based on, e.g.,ontology 760. Ontology 760 is a hierarchical structure containing manynodes, each node representing either an “actionable intent” or a“property” relevant to one or more of the “actionable intents” or other“properties.” As noted above, an “actionable intent” represents a taskthat the digital assistant is capable of performing, i.e., it is“actionable” or can be acted on. A “property” represents a parameterassociated with an actionable intent or a sub-aspect of anotherproperty. A linkage between an actionable intent node and a propertynode in ontology 760 defines how a parameter represented by the propertynode pertains to the task represented by the actionable intent node.

In some examples, ontology 760 is made up of actionable intent nodes andproperty nodes. Within ontology 760, each actionable intent node islinked to one or more property nodes either directly or through one ormore intermediate property nodes. Similarly, each property node islinked to one or more actionable intent nodes either directly or throughone or more intermediate property nodes. For example, as shown in FIG.7C, ontology 760 includes a “restaurant reservation” node (i.e., anactionable intent node). Property nodes “restaurant,” “date/time” (forthe reservation), and “party size” are each directly linked to theactionable intent node (i.e., the “restaurant reservation” node).

In addition, property nodes “cuisine,” “price range,” “phone number,”and “location” are sub-nodes of the property node “restaurant,” and areeach linked to the “restaurant reservation” node (i.e., the actionableintent node) through the intermediate property node “restaurant.” Foranother example, as shown in FIG. 7C, ontology 760 also includes a “setreminder” node (i.e., another actionable intent node). Property nodes“date/time” (for setting the reminder) and “subject” (for the reminder)are each linked to the “set reminder” node. Since the property“date/time” is relevant to both the task of making a restaurantreservation and the task of setting a reminder, the property node“date/time” is linked to both the “restaurant reservation” node and the“set reminder” node in ontology 760.

An actionable intent node, along with its linked concept nodes, isdescribed as a “domain.” In the present discussion, each domain isassociated with a respective actionable intent, and refers to the groupof nodes (and the relationships there between) associated with theparticular actionable intent. For example, ontology 760 shown in FIG. 7Cincludes an example of restaurant reservation domain 762 and an exampleof reminder domain 764 within ontology 760. The restaurant reservationdomain includes the actionable intent node “restaurant reservation,”property nodes “restaurant,” “date/time,” and “party size,” andsub-property nodes “cuisine,” “price range,” “phone number,” and“location.” Reminder domain 764 includes the actionable intent node “setreminder,” and property nodes “subject” and “date/time.” In someexamples, ontology 760 is made up of many domains. Each domain sharesone or more property nodes with one or more other domains. For example,the “date/time” property node is associated with many different domains(e.g., a scheduling domain, a travel reservation domain, a movie ticketdomain, etc.), in addition to restaurant reservation domain 762 andreminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, otherdomains include, for example, “find a movie,” “initiate a phone call,”“find directions,” “schedule a meeting,” “send a message,” and “providean answer to a question,” “read a list,” “providing navigationinstructions,” “provide instructions for a task” and so on. A “send amessage” domain is associated with a “send a message” actionable intentnode, and further includes property nodes such as “recipient(s),”“message type,” and “message body.” The property node “recipient” isfurther defined, for example, by the sub-property nodes such as“recipient name” and “message address.”

In some examples, ontology 760 includes all the domains (and henceactionable intents) that the digital assistant is capable ofunderstanding and acting upon. In some examples, ontology 760 ismodified, such as by adding or removing entire domains or nodes, or bymodifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionableintents are clustered under a “super domain” in ontology 760. Forexample, a “travel” super-domain includes a cluster of property nodesand actionable intent nodes related to travel. The actionable intentnodes related to travel includes “airline reservation,” “hotelreservation,” “car rental,” “get directions,” “find points of interest,”and so on. The actionable intent nodes under the same super domain(e.g., the “travel” super domain) have many property nodes in common.For example, the actionable intent nodes for “airline reservation,”“hotel reservation,” “car rental,” “get directions,” and “find points ofinterest” share one or more of the property nodes “start location,”“destination,” “departure date/time,” “arrival date/time,” and “partysize.”

In some examples, each node in ontology 760 is associated with a set ofwords and/or phrases that are relevant to the property or actionableintent represented by the node. The respective set of words and/orphrases associated with each node are the so-called “vocabulary”associated with the node. The respective set of words and/or phrasesassociated with each node are stored in vocabulary index 744 inassociation with the property or actionable intent represented by thenode. For example, returning to FIG. 7B, the vocabulary associated withthe node for the property of “restaurant” includes words such as “food,”“drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” andso on. For another example, the vocabulary associated with the node forthe actionable intent of “initiate a phone call” includes words andphrases such as “call,” “phone,” “dial,” “ring,” “call this number,”“make a call to,” and so on. The vocabulary index 744 optionallyincludes words and phrases in different languages.

Natural language processing module 732 receives the candidate textrepresentations (e.g., text string(s) or token sequence(s)) from STTprocessing module 730, and for each candidate representation, determineswhat nodes are implicated by the words in the candidate textrepresentation. In some examples, if a word or phrase in the candidatetext representation is found to be associated with one or more nodes inontology 760 (via vocabulary index 744), the word or phrase “triggers”or “activates” those nodes. Based on the quantity and/or relativeimportance of the activated nodes, natural language processing module732 selects one of the actionable intents as the task that the userintended the digital assistant to perform. In some examples, the domainthat has the most “triggered” nodes is selected. In some examples, thedomain having the highest confidence value (e.g., based on the relativeimportance of its various triggered nodes) is selected. In someexamples, the domain is selected based on a combination of the numberand the importance of the triggered nodes. In some examples, additionalfactors are considered in selecting the node as well, such as whetherthe digital assistant has previously correctly interpreted a similarrequest from a user.

User data 748 includes user-specific information, such as user-specificvocabulary, user preferences, user address, user's default and secondarylanguages, user's contact list, and other short-term or long-terminformation for each user. In some examples, natural language processingmodule 732 uses the user-specific information to supplement theinformation contained in the user input to further define the userintent. For example, for a user request “invite my friends to mybirthday party,” natural language processing module 732 is able toaccess user data 748 to determine who the “friends” are and when andwhere the “birthday party” would be held, rather than requiring the userto provide such information explicitly in his/her request.

It should be recognized that in some examples, natural languageprocessing module 732 is implemented using one or more machine learningmechanisms (e.g., neural networks). In particular, the one or moremachine learning mechanisms are configured to receive a candidate textrepresentation and contextual information associated with the candidatetext representation. Based on the candidate text representation and theassociated contextual information, the one or more machine learningmechanism are configured to determine intent confidence scores over aset of candidate actionable intents. Natural language processing module732 can select one or more candidate actionable intents from the set ofcandidate actionable intents based on the determined intent confidencescores. In some examples, an ontology (e.g., ontology 760) is also usedto select the one or more candidate actionable intents from the set ofcandidate actionable intents.

Other details of searching an ontology based on a token string isdescribed in U.S. Utility application Ser. No. 12/341,743 for “Methodand Apparatus for Searching Using An Active Ontology,” filed Dec. 22,2008, the entire disclosure of which is incorporated herein byreference.

In some examples, once natural language processing module 732 identifiesan actionable intent (or domain) based on the user request, naturallanguage processing module 732 generates a structured query to representthe identified actionable intent. In some examples, the structured queryincludes parameters for one or more nodes within the domain for theactionable intent, and at least some of the parameters are populatedwith the specific information and requirements specified in the userrequest. For example, the user says “Make me a dinner reservation at asushi place at 7.” In this case, natural language processing module 732is able to correctly identify the actionable intent to be “restaurantreservation” based on the user input. According to the ontology, astructured query for a “restaurant reservation” domain includesparameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and thelike. In some examples, based on the speech input and the text derivedfrom the speech input using STT processing module 730, natural languageprocessing module 732 generates a partial structured query for therestaurant reservation domain, where the partial structured queryincludes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, inthis example, the user's utterance contains insufficient information tocomplete the structured query associated with the domain. Therefore,other necessary parameters such as {Party Size} and {Date} is notspecified in the structured query based on the information currentlyavailable. In some examples, natural language processing module 732populates some parameters of the structured query with receivedcontextual information. For example, in some examples, if the userrequested a sushi restaurant “near me,” natural language processingmodule 732 populates a (location) parameter in the structured query withGPS coordinates from the user device.

In some examples, natural language processing module 732 identifiesmultiple candidate actionable intents for each candidate textrepresentation received from STT processing module 730. Further, in someexamples, a respective structured query (partial or complete) isgenerated for each identified candidate actionable intent. Naturallanguage processing module 732 determines an intent confidence score foreach candidate actionable intent and ranks the candidate actionableintents based on the intent confidence scores. In some examples, naturallanguage processing module 732 passes the generated structured query (orqueries), including any completed parameters, to task flow processingmodule 736 (“task flow processor”). In some examples, the structuredquery (or queries) for the m-best (e.g., m highest ranked) candidateactionable intents are provided to task flow processing module 736,where m is a predetermined integer greater than zero. In some examples,the structured query (or queries) for the m-best candidate actionableintents are provided to task flow processing module 736 with thecorresponding candidate text representation(s).

Other details of inferring a user intent based on multiple candidateactionable intents determined from multiple candidate textrepresentations of a speech input are described in U.S. Utilityapplication Ser. No. 14/298,725 for “System and Method for InferringUser Intent From Speech Inputs,” filed Jun. 6, 2014, the entiredisclosure of which is incorporated herein by reference.

Task flow processing module 736 is configured to receive the structuredquery (or queries) from natural language processing module 732, completethe structured query, if necessary, and perform the actions required to“complete” the user's ultimate request. In some examples, the variousprocedures necessary to complete these tasks are provided in task flowmodels 754. In some examples, task flow models 754 include proceduresfor obtaining additional information from the user and task flows forperforming actions associated with the actionable intent.

As described above, in order to complete a structured query, task flowprocessing module 736 needs to initiate additional dialogue with theuser in order to obtain additional information, and/or disambiguatepotentially ambiguous utterances. When such interactions are necessary,task flow processing module 736 invokes dialogue flow processing module734 to engage in a dialogue with the user. In some examples, dialogueflow processing module 734 determines how (and/or when) to ask the userfor the additional information and receives and processes the userresponses. The questions are provided to and answers are received fromthe users through I/O processing module 728. In some examples, dialogueflow processing module 734 presents dialogue output to the user viaaudio and/or visual output, and receives input from the user via spokenor physical (e.g., clicking) responses. Continuing with the exampleabove, when task flow processing module 736 invokes dialogue flowprocessing module 734 to determine the “party size” and “date”information for the structured query associated with the domain“restaurant reservation,” dialogue flow processing module 734 generatesquestions such as “For how many people?” and “On which day?” to pass tothe user. Once answers are received from the user, dialogue flowprocessing module 734 then populates the structured query with themissing information, or pass the information to task flow processingmodule 736 to complete the missing information from the structuredquery.

Once task flow processing module 736 has completed the structured queryfor an actionable intent, task flow processing module 736 proceeds toperform the ultimate task associated with the actionable intent.Accordingly, task flow processing module 736 executes the steps andinstructions in the task flow model according to the specific parameterscontained in the structured query. For example, the task flow model forthe actionable intent of “restaurant reservation” includes steps andinstructions for contacting a restaurant and actually requesting areservation for a particular party size at a particular time. Forexample, using a structured query such as: {restaurant reservation,restaurant=ABC Café, date=Mar. 12, 2012, time=7 pm, party size=5}, taskflow processing module 736 performs the steps of: (1) logging onto aserver of the ABC Café or a restaurant reservation system such asOPENTABLE®, (2) entering the date, time, and party size information in aform on the website. (3) submitting the form, and (4) making a calendarentry for the reservation in the user's calendar.

In some examples, task flow processing module 736 employs the assistanceof service processing module 738 (“service processing module”) tocomplete a task requested in the user input or to provide aninformational answer requested in the user input. For example, serviceprocessing module 738 acts on behalf of task flow processing module 736to make a phone call, set a calendar entry, invoke a map search, invokeor interact with other user applications installed on the user device,and invoke or interact with third-party services (e.g., a restaurantreservation portal, a social networking website, a banking portal,etc.). In some examples, the protocols and application programminginterfaces (API) required by each service are specified by a respectiveservice model among service models 756. Service processing module 738accesses the appropriate service model for a service and generaterequests for the service in accordance with the protocols and APIsrequired by the service according to the service model.

For example, if a restaurant has enabled an online reservation service,the restaurant submits a service model specifying the necessaryparameters for making a reservation and the APIs for communicating thevalues of the necessary parameter to the online reservation service.When requested by task flow processing module 736, service processingmodule 738 establishes a network connection with the online reservationservice using the web address stored in the service model, and send thenecessary parameters of the reservation (e.g., time, date, party size)to the online reservation interface in a format according to the API ofthe online reservation service.

In some examples, natural language processing module 732, dialogue flowprocessing module 734, and task flow processing module 736 are usedcollectively and iteratively to infer and define the user's intent,obtain information to further clarify and refine the user intent, andfinally generate a response (i.e., an output to the user, or thecompletion of a task) to fulfill the user's intent. The generatedresponse is a dialogue response to the speech input that at leastpartially fulfills the user's intent. Further, in some examples, thegenerated response is output as a speech output. In these examples, thegenerated response is sent to speech synthesis module 740 (e.g., speechsynthesizer) where it can be processed to synthesize the dialogueresponse in speech form. In yet other examples, the generated responseis data content relevant to satisfying a user request in the speechinput.

In examples where task flow processing module 736 receives multiplestructured queries from natural language processing module 732, taskflow processing module 736 initially processes the first structuredquery of the received structured queries to attempt to complete thefirst structured query and/or execute one or more tasks or actionsrepresented by the first structured query. In some examples, the firststructured query corresponds to the highest ranked actionable intent. Inother examples, the first structured query is selected from the receivedstructured queries based on a combination of the corresponding speechrecognition confidence scores and the corresponding intent confidencescores. In some examples, if task flow processing module 736 encountersan error during processing of the first structured query (e.g., due toan inability to determine a necessary parameter), the task flowprocessing module 736 can proceed to select and process a secondstructured query of the received structured queries that corresponds toa lower ranked actionable intent. The second structured query isselected, for example, based on the speech recognition confidence scoreof the corresponding candidate text representation, the intentconfidence score of the corresponding candidate actionable intent, amissing necessary parameter in the first structured query, or anycombination thereof.

Speech synthesis module 740 is configured to synthesize speech outputsfor presentation to the user. Speech synthesis module 740 synthesizesspeech outputs based on text provided by the digital assistant. Forexample, the generated dialogue response is in the form of a textstring. Speech synthesis module 740 converts the text string to anaudible speech output. Speech synthesis module 740 uses any appropriatespeech synthesis technique in order to generate speech outputs fromtext, including, but not limited, to concatenative synthesis, unitselection synthesis, diphone synthesis, domain-specific synthesis,formant synthesis, articulatory synthesis, hidden Markov model (HMM)based synthesis, and sinewave synthesis. In some examples, speechsynthesis module 740 is configured to synthesize individual words basedon phonemic strings corresponding to the words. For example, a phonemicstring is associated with a word in the generated dialogue response. Thephonemic string is stored in metadata associated with the word. Speechsynthesis model 740 is configured to directly process the phonemicstring in the metadata to synthesize the word in speech form.

In some examples, instead of (or in addition to) using speech synthesismodule 740, speech synthesis is performed on a remote device (e.g., theserver system 108), and the synthesized speech is sent to the userdevice for output to the user. For example, this can occur in someimplementations where outputs for a digital assistant are generated at aserver system. And because server systems generally have more processingpower or resources than a user device, it is possible to obtain higherquality speech outputs than would be practical with client-sidesynthesis.

Additional details on digital assistants can be found in the U.S.Utility application Ser. No. 12/987,982, entitled “Intelligent AutomatedAssistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No.13/251,088, entitled “Generating and Processing Task Items ThatRepresent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosuresof which are incorporated herein by reference.

4. Exemplary Architecture and Functionality of a Virtual Assistant

FIG. 8 illustrates a block diagram of a digital assistant 800 fortraining a machine-learning translation system and/or for performingtranslation using the trained machine-learning translation system. Insome examples, digital assistant 800 (e.g., digital assistant system700) can be implemented by a user device according to variousembodiments. In some embodiments, the user device, a server (e.g.,server 108), or a combination thereof, can implement digital assistant800. The user device can be implemented using, for example, device 104,200, or 400 as illustrated in FIGS. 1, 2A-2B, and 4. In someembodiments, digital assistant 800 can be implemented using digitalassistant module 726 of digital assistant system 700 shown in FIG. 7B.For example, digital assistant 800 can include one or more modules,models, applications, vocabularies, and user data similar to those ofdigital assistant module 726. With reference to FIG. 7B, as one example,digital assistant 800 can include, for example, one or more of STTprocessing module 730, phonetic alphabet conversion module 731,vocabulary 744, user data 748, and/or natural language processing module760. One or more of these modules (e.g., natural language processingmodule 760) can be implemented using a machine-learning based system.Digital assistant 800 can also include modules, models, applications,vocabularies, and user data that are not included in digital assistantmodule 726. For example, as shown in FIG. 9A, digital assistant 800 canalso include a machine-learning translation system 910.

With reference to FIG. 8, in some embodiments, digital assistant 800operating on an electronic device receives, via a microphone, a speechinput 802. In response, digital assistant 800 determines whether thereceived speech input represents a user request for translation. In someembodiments, using one or more modules depicted in FIG. 7B (e.g., STTprocessing module 730, phonetic alphabet conversion module 731,vocabulary 744, user data 748, and/or natural language processing module760), digital assistant 800 interprets speech input 802 to derive a userintent as described above. Digital assistant 800 further determineswhether the user intent is associated with a translation domain. Forexample, speech input 802 may include a user utterance such as “How doyou say ‘Good Morning’ in German?” Digital assistant 800 performsnatural language processing based on an ontology (e.g., ontology 760)and determines that the user intent is associated with a translationdomain (e.g., based on one or more tokens such as “translate,” “how do Isay,” “can you translate,” or the like). In some embodiments, digitalassistant 800 determines whether the received speech input represents auser request for translation using a machine-learning translationsystem, which is trained to recognize different types of user requestsfor translation and to provide responses accordingly.

FIG. 9A is a block diagram illustrating training of a machine-learningtranslation system 910 of digital assistant 800. As shown in FIG. 9A, insome embodiments, digital assistant 800 includes a training data adaptor906. Training data adaptor 906 obtains a first set of training data 902and one or more templates 904. Based on first set of training data 902and templates 904, training data adaptor 906 generates a second set oftraining data 908 for training machine-learning translation system 910.

FIG. 9B is a block diagram illustrating generation of second set oftraining data 908 for training machine-learning translation system 910.As illustrated in FIG. 9B, training data adaptor 906 obtains first setof training data 902 from various data sources 922. Data sources 922 caninclude, for example, past user translation requests 922A of aparticular user, translation requests from other users 922B,internal/external text corpus 922C, or the like. Past user translationrequests 922A of the particular user and translation requests from otherusers 922B include, for example, translation requests that have beenreceived and/or processed in the past. An internal text corpus 922Cincludes, for example, a collection of words, phrases, and sentencesstored in or accessible by the electronic device on which digitalassistant 800 operates. An external text corpus 922C includes, forexample, an online dictionary, a Wikipedia website, or any other sourcesof words, phrases, and sentences.

As shown in FIG. 9B, first set of training data 902 includes one or morepayloads in a first language and a corresponding translation in a secondlanguage. A payload can include one or more tokens such as a word, aphrase, and a sentence. For example, first set of training data 902includes a data item 902A, which includes a word “cat” in English andits translation in German “eine Katze.” As another example, first set oftraining data 902 includes a data item 902B, which includes a phrase“Good morning” in English and its translation in German “Guten Morgen.”As another example, first set of training data 902 includes a data item902C, which includes a sentence “how are you” in English and itstranslation in German “Wie geht es dir?” It is appreciated that firstset of training data 902 can include any number of payloads (e.g.,words, phrases, and sentences) in any language and their correspondingtranslation in any other languages. For example, for the three dataitems 902A-C, first set of training data 902 can also include theircorresponding translations in languages other than German (e.g., French,Chinese, Japanese, etc.).

With reference to FIG. 9B, in some embodiments, to generate second setof training data 908, training data adaptor 906 obtains templates 904.Templates 904 can be used to adapt first set of training data 902. Atemplate includes one or more prefixes and one or more variables. Asexamples shown in FIG. 9B, template 904A can be “How do I say ${payload}in ${target}?”; template 904B can be “Hey Siri how do you say ${payload}in ${target}?”; and template 904C can be “What is ${payload} in${target}?” In templates 904A-C, “How do I say,” “Hey Siri how do yousay,” and “What is” are prefixes; and ${payload} and ${target} arevariables. In some embodiments, a prefix can include one or more tokensrepresenting a partial request for translation, such as “How do I say,”“Translate,” “Can you say,” “What is,” or the like. The partial requestfor translation may be explicit request for translation (e.g., “How do Isay”) or implicit request for translation (e.g., the “How do I” in thecomplete request such as “How do I order Margarita in Spanish?”). Aprefix can also include a trigger phrase for invoking a digitalassistant (e.g., “Hey Siri”, “Hey Assistant”, or the like).

In some embodiments, a prefix in a template can also include one or moretokens that are substantially identical to one or more tokens includedin a payload. For example, a payload (e.g., the phrase or sentenceto-be-translated) may include tokens such as “How do you say shakehands?” or “What is a HomePod?” A prefix in a template can also includetokens such as “How do you say,” or “What is.” Thus, the prefix mayrepeat some of the tokens in the payload. As described in more detailbelow, including such a prefix in a template can facilitate processing auser request for translation such as “How do you say ‘How do you sayshake hands’ in German?” or “What is ‘what is a HomePod’ in German?”Current techniques for translating such a user request would ignore someof the tokens and thus result in an erroneous or inaccurate translation.For example, in processing a user request “How do you say ‘How do yousay shake hands’ in German?”, current techniques would parse the userrequest and ignore the second “How do you say” and simply identify“shake hands” (as opposed to “How do you say shake hands?”) as thepayload. As described in more detail below, the machine-learning basedtechniques described in this application process the entire user requestand generate a translation of the payload without explicitly identifyingthe payload. The machine-learning based techniques thus do noterroneously ignore tokens that are part of the payload.

With reference to FIG. 9B, ${payload} and ${target} are variables intemplates 904A-C. Variable ${payload} represents a to-be-translatedpayload in the first language (e.g., “Good morning” in English).Variable ${target} represents a target translation language (e.g.,German).

As shown in FIG. 9B, based on templates 904, training data adaptor 906adapts first set of training data 902 to generate second set of trainingdata 908. As an example, using template 904A (e.g., “How do I say${payload} in {target}?”, training data adaptor 906 adapts data item902A in first set of training data 902 to generate adapted data item924A in second set of training data 924. In some embodiments, to performsuch adaptation, training data adaptor 906 replaces variable“${payload}” in template 904A with the payload “cat” in data item 902A;and replaces variable “${target}” in template 904A with the secondlanguage in the first set of training data 902 (e.g., German). As aresult of the adaptation, training data adaptor 906 generates an adaptedpayload such as “How do I say cat in German?” Training data adaptor 906,in some embodiments, replaces the payload in data item 902A (e.g. “cat”)with the adapted payload (e.g., “How do I say cat in German?”) togenerate an adapted data item 924A in second set of training data 908(e.g., {“How do I say cat in German?”} {“eine Katze.”}).

Similarly, with reference to FIG. 9B, for each data item 902A-C in firstset of training data 902, training data adaptor 906 generates acorresponding adapted data item in second set of training data 908. Asan example, for data item 902A and using template 904B, training dataadaptor 906 generates an adapted payload such as “Hey Siri, how do yousay cat in German?”; and replace the payload in data item 902A (e.g.“cat”) with the adapted payload (e.g., “Hey Siri, do you say cat inGerman?”) to generate an adapted data item 924B in second set oftraining data 908 (e.g., {“Hey Siri, how do you say cat in German?”}{“eine Katze.”}). As another example, for data item 902A and usingtemplate 904C, training data adaptor 906 generates an adapted payloadsuch as “What is cat in German?”; and replace the payload in data item902A (e.g. “cat”) with the adapted payload (e.g., “What is cat inGerman?”) to generate an adapted data item 924C in second set oftraining data 908 (e.g., {“Hey Siri, how do you say cat in German?”}{“eine Katze.”}).

As another example, for data item 902B and using each of the templates904A-C, training data adaptor 906 generates adapted data items 926A-C(e.g., {“How do I say good morning in German?”} {“Guten Morgen”}; {“HeySiri, how do you say good morning in German?”} {“Guten Morgen”}; and{“What is good morning in German?”} {“Guten Morgen”}).

As another example, for data item 902C and using each of the templates904A-C, training data adaptor 906 generates adapted data items 928A-C(e.g., {“How do I say ‘how are you’ in German?”} {“Wie Geht es dir”};{“Hey Siri, how do you say ‘how are you’ in German?”} {“Wie Geht esdir”}; and {“What is how are you in German?”} {“Wie Geht es dir”}).

The second set of training data 908 thus includes adapted data itemsincluding adapted payloads that are formulated as translation requests(e.g., “How do I say ‘how are you’ in German?”). Using such adaptedpayloads in the training data for training a machine-learningtranslation system reduces or eliminates the need for parsing a userrequest for translation to accurately identify the payload. Rather, thesystem can process the entire user request for translation withouthaving to parse or accurately identify the payload. Further, asdescribed above, typical translation systems perform multiple stepsincluding parsing of the user request, identifying of the payload, andidentifying of the target translation language. The multiple steps maybe performed by different systems (e.g., a natural language parserperforms the step of parsing the user request, where a user intentinferring system identifies the payload and/or the target translationlanguage). As a result, there is a multiplying of errors from differentsystems and an erroneous translation may result if any of the multiplesystems performs an erroneous or inaccurate step. In contrast, the errorof the machine-learning translation system trained with second set oftraining data 908 is attributed to a single system and thereforeeliminates the multiplying of errors from multiple systems. Using themachine-learning translation system trained with the second set oftraining data 908 thus improves the accuracy of the translation, and inturn improves the operational efficiency.

In some embodiments, a user request for translation may be incomplete.For example, the user request may not include a payload and/or a targettranslation language. With reference to FIG. 9C, templates 904 caninclude one or more templates for generating training data inanticipation of receiving such incomplete user requests. As shown inFIG. 9C, similar to those described above, training data adaptor 906obtains first set of training data 902 and templates 904. Templates 904can include, for example, a template 904A such as “Into which languagedo you want ${payload} to be translated?” Template 904A can be used forgenerating training data in anticipation that the machine-learningtranslation system receives an incomplete user request such as “How do Isay ‘good morning?” ’, which does not include a target translationlanguage.

Similar to those described above, using the payloads included in thefirst set of training data 902 (e.g., “Cat,” “Good morning,” and “Howare you?”) and template 904A, training data adaptor 906 generatesinquiries 934 for obtaining a target translation language. As shown inFIG. 9C, inquiries 934 include, for example, an inquiry 934A such as“Into which language do you want ‘cat’ to be translated?”; an inquiry934B such as “Into which language do you want ‘good morning’ to betranslated?”, and an inquiry 934C such as “Into which language do youwant ‘how are you’ to be translated?”. It is appreciated that inquiries934 can include any other inquires using other payloads included infirst set of training data 902. Inquiries 934 can be included in secondset of training data 908, which is used for training themachine-learning translation system (e.g., system 910 shown in FIG. 9A).

With reference to FIG. 9C, templates 904 can include, for example, atemplate 904B such as “Sure, what do you want to be translated into${target}?” Template 904B is used for generating training data inanticipation that the machine-learning translation system receives anincomplete user request such as “Can you translate into German?”, whichdoes not include a payload to-be-translated. Similar to those describedabove, using the second language included in first set of training data902 (e.g., German) and template 904B, training data adaptor 906generates inquiries 936 to obtain a payload. As shown in FIG. 9C,inquiries 936 include, for example, an inquiry 936A such as “Sure, whatdo you want to be translated into German?” It is appreciated thatinquiries 936 can include any other inquires using other languagesassociated with the translations included in first set of training data902 (e.g., French, Chinese, Japanese). Inquiries 936 can be included insecond set of training data 908, which is used for training themachine-learning translation system (e.g., system 910 shown in FIG. 9A).

With reference to FIG. 9C, templates 904 can include, for example, atemplate 904C such as “Sure, what do you want to be translated and intowhat language?” Template 904C is used for generating training data inanticipation that the machine-learning translation system receives anincomplete user request such as “Hey Siri, translate,” which does notinclude either a payload to-be-translated or a target translationlanguage. In some embodiments, training data adaptor 906 generates aninquiry 938 to obtain both a payload and a target translation language.As shown in FIG. 9C, inquiry 938 can include, for example, “Sure, whatdo you want to be translated and into which language?” Inquiries 938 canbe included in second set of training data 908, which is used fortraining the machine-learning translation system (e.g., system 910 shownin FIG. 9A).

With reference back to FIG. 9A, training data adaptor 906 providessecond set of training data 908 to machine-learning translation system910 for training the system. In some embodiments, machine-learningtranslation system 910 is a neural machine translation (NMT) system. Forexample, machine-learning translation system 910 can have anencoder-decoder network with multiple layers of Recurrent Neural Network(RNN) with Long Short-Term Memory hidden units. In such a system, inputs(e.g., the adapted payloads in second set of training data 908 or a userrequest for translation) are mapped to word vectors and then provided toa bidirectional encoder (e.g., one or more LSTM encoders). Thebidirectional encoder generates an output sequence, based on which RNNdecoder predicts a target sequence for generating the translation of theinputs. Details of an exemplary NMT system can be found in “DomainControl for Neural Machine Translation,” by Kobus et al, SYSTRAN/5 rueFeydeau, 75002 Paris, France, the entire content of which isincorporated by reference for all purposes.

FIGS. 10A-10C illustrates exemplary digital assistant 800 for performingtranslation using a trained machine-learning translation system 910. Asdescribed above, machine-learning translation system 910 can be trainedwith second set of training data 908, which include adapted payloads andinquiries. The trained machine-learning translation system 910 can thusperform translation without explicitly parsing a user request fortranslation to identify a payload. As shown in FIG. 10A, user 810provides speech input 1002 such as “How do you say ‘Good Morning’ inGerman?” Similar to those described above, in response to receivingspeech input 1002, digital assistant 800 determines whether the receivedspeech input 1002 represents a user request for translation (e.g.,determines that the user intent derived from speech input 1002 isassociated with a translation domain).

In some embodiments, in accordance with a determination that thereceived speech input represents a user request for translation, digitalassistant 800 provides a representation of the received speech input(e.g., a text representation of “How do you say ‘Good Morning’ inGerman?”) to the trained machine-learning translation system 910 (e.g.,trained with second set of training data 908). In some embodiments, arepresentation of the entire speech input is provided tomachine-learning translation system 910 trained with second set oftraining data 908. As described above, second set of training data 908includes one or more adapted payloads formulated as translation requests(e.g., “How do I say ‘good morning’ in German?”, “Hey Siri, how do yousay ‘good morning’ in German?”, and “What is ‘ask for help’ inGerman?”). Therefore, trained machine-learning translation system 910can process the representation of the entire speech input 1002 withoutbeing required to parse speech input 1002 to explicitly identify apayload to-be-translated.

In some embodiments, as shown in FIG. 10A, the trained machine-learningtranslation system 910 of digital assistant 800 obtains a response tothe user request for translation included in speech input 1002. In someembodiments, to obtain a response, the trained machine-learningtranslation system 910 of digital assistant 800 determines, withoutparsing the received speech input 1002 to identify a to-be-translatedpayload, whether the user request for translation includes at least oneto-be-translated payload (e.g., “good morning”) and a target translationlanguage (e.g., German). For example, machine-learning translationsystem 910 is trained with second set of training data 908, whichincludes a data item that is substantially identical to therepresentation of speech input 1002 (e.g., a text representation of “Howdo you say ‘Good Morning’ in German?”). As a result, trainedmachine-learning translation system 910 determines that a predictedtranslation can be provided with a high confidence (e.g., a confidencelevel that exceeds a threshold). Accordingly, trained machine-learningtranslation system 910 predicts a response including a translation suchas “Guten Morgen.” And digital assistant 800 provides an audio and/orvisual output 1004 including the predicted response.

While FIG. 10A illustrates that machine-learning translation system 910is trained to process an explicit translation request such as “How doyou say ‘Good morning’ in German?”, machine-learning translation system910 can also be trained to process an implicit translation request(e.g., “How do I order a Margarita in Spanish?”) without parsing thetranslation request to explicitly identify the to-be-translated payloadand/or the target translation language. For example, a template can alsoinclude “How do I ${payload} in ${target}?”, where a first set oftraining data can include a data item ({“I want to order a Margarita.”}{“Quiero ordenar a Margarita.”}). Thus, a training data adaptor cangenerate a data item such as ({“How do I order Margarita in Spanish?”}{“Quiero ordenar a Margarita.”}) and include the data item in a secondset of training data for training machine-learning translation system910. Trained machine-learning translation system 910 can thus processthe entire translation request and recognize the implicit translationrequest (e.g., “How do I order a Margarita in Spanish?”), withoutexplicitly identifying the to-be-translated payload and/or the targettranslation language.

As described above, conventional translation systems may have difficultyin translating a user request where some of the tokens in a payload arerepeated as part of the translation request. For example, a user requestfor translation may be “How do you say ‘how do you say shake hands’ inGerman?” or “What is ‘what is a HomePod’ in German?”. Conventionaltranslation systems may erroneously ignore part of the payload (e.g.,ignore the second “how do you say” or ignore the second “what is”).Trained machine-learning translation system 910 described in thisapplication can avoid such an error and process such user requests togenerate a correct response because it does not need to parse thetranslation request to explicitly identify the to-be-translated payloadand/or the target translation language. For example, a template fortraining machine-learning translation system 910 can include “How do yousay ${payload} in ${target}?”, where a first set of training data caninclude a data item ({“How do you say ‘shake hands’?”} {“Wie sagt man,Hände schütteln?”}). Thus, a training data adaptor can generate a dataitem such as ({“How do you say ‘how do you say ‘shake hands’ in German?}{“Wie sagt man, Hände schütteln?”}) and include the data item in asecond set of training data for training machine-learning translationsystem 910. Trained machine-learning translation system 910 can thusprocess the entire translation request and generate a correct responsewithout explicitly identifying the to-be-translated payload and/or thetarget translation language.

As described above, in some embodiments, machine-learning translationsystem 910 of digital assistant 800 can also be trained to processincomplete user requests. As an example shown in FIG. 10B, digitalassistant 800 receives a speech input 1012 such as “How do you say ‘goodmorning’?” Similar to those described above, based on a determinationthat the received speech input 1012 represents a user request fortranslation, digital assistant 800 provides a representation of receivedspeech input 1012 (e.g., a text representation of “How do you say ‘GoodMorning’?”) to the trained machine-learning translation system 910(e.g., trained with second set of training data 908).

In some embodiments, as described above, to obtain a response, trainedmachine-learning translation system 910 of digital assistant 800determines, without parsing the received speech input to identify ato-be-translated payload, whether the user request for translationincludes at least one to-be-translated payload (e.g., “good morning”)and a target translation language (e.g., German). In the example shownin FIG. 10B, machine-learning translation system 910 is trained withsecond set of training data 908, which includes a data item that includeboth the to-be-translated payload and the target translation language(e.g., a text representation of “How do you say ‘Good Morning’ inGerman?”). Trained machine-learning translation system 910 can thusdetermine that the user request for translation included in speech input1012 includes the to-be-translated payload but does not include a targettranslation language.

Based on such a determination, trained machine-learning translationsystem 910 identifies a first inquiry to obtain the target translationlanguage. As described above in FIG. 9C, second set of training data 908includes a data item 934B such as “Into which language do you want ‘goodmorning’ to be translated?”. Therefore, trained machine-learningtranslation system 910 identifies a first inquiry similar to data item934B (e.g., “Into which language do you want ‘good morning’ to betranslated?”) and digital assistant 800 outputs an audio 1014 of thefirst inquiry, as shown in FIG. 10B. In another example, speech input1012 may include more than one target translation language (e.g., “Howdo you say ‘good morning’ in German in French?”). In some embodiments,trained machine-learning translation system 910 can also determine thatthe user request for translation included in speech input 1012 does notinclude a single target translation language, and therefore identifiesthe first inquiry (e.g., “Into which language do you want ‘good morning’to be translated?”) to be outputted to user 810.

Digital assistant 800 receives, for example, a first user response 1016to the first inquiry (e.g., German). Based on first user response 1016,digital assistant 800 identifies the target translation language (e.g.,German). In some embodiments, digital assistant 800 can store arepresentation of the previously received speech input 1012, whichincludes the to-be-translated payload (e.g., “How do you say ‘goodmorning’?”). Based on the previously-stored representation of speechinput 1012 and a representation of first user response 1016, the trainedmachine-learning translation system 910 obtains a translation of theto-be-translated payload. For example, based on the combinedrepresentations of “How do you say ‘good morning’,” and “German”, thetrained machine-learning translation system 910 predicts, with highconfidence, that the translation is “Guten Morgen.” Accordingly, trainedmachine-learning translation system 910 predicts a response including atranslation such as “Guten Morgen.” And digital assistant 800 providesan audio and/or visual output 1018 including the predicted response1018.

FIG. 10C illustrates another example of processing an incomplete userrequest for translation. As shown in FIG. 10C, digital assistant 800receives a speech input 1022 such as “Can you translate into German?”Similar to those described above, based on a determination that thereceived speech input 1022 represents a user request for translation,digital assistant 800 provides a representation of the received speechinput 1022 (e.g., a text representation of “Can you translate intoGerman?”) to trained machine-learning translation system 910 (e.g.,trained with second set of training data 908).

In some embodiments, as described above, to obtain a response, trainedmachine-learning translation system 910 of digital assistant 800determines, without parsing the received speech input to identify ato-be-translated payload, whether the user request for translationincludes at least one to-be-translated payload (e.g., “good morning”)and a target translation language (e.g., German). In the example shownin FIG. 10C, machine-learning translation system 910 is trained withsecond set of training data 908, which includes a data item that includeboth the to-be-translated payload and the target translation language(e.g., a text representation of “How do you say ‘Good Morning’ inGerman?”). Trained machine-learning translation system 910 can thusdetermine that the user request for translation included in speech input1022 includes the target translation language but does not include ato-be-translated payload.

Based on such a determination, trained machine-learning translationsystem 910 identifies a second inquiry to obtain the to-be-translatedpayload. As described above in FIG. 9C, second set of training data 908includes a data item 936 such as “Sure, what do you want to betranslated into German?” Therefore, trained machine-learning translationsystem 910 identifies a second inquiry similar to data item 936 anddigital assistant 800 outputs an audio 1024 of the second inquiry, asshown in FIG. 10C. Digital assistant 800 receives, for example, a seconduser response 1026 to the second inquiry (e.g., “Good morning”). Basedon second user response 1026, digital assistant 800 identifies theto-be-translated payload (e.g., “Good Morning”). In some embodiments,digital assistant 800 can store a representation of the previouslyreceived speech input 1022, which includes the target translationlanguage (e.g., “Can you translate into German?”). Based on thepreviously-stored representation of speech input 1022 and arepresentation of second user response 1026, the trainedmachine-learning translation system 910 obtains a translation of theto-be-translated payload. For example, based on the combinedrepresentations of “Can you translate into German?” and “Good morning”,the trained machine-learning translation system 910 predicts, with highconfidence, that the translation is “Guten Morgen.” Accordingly, trainedmachine-learning translation system 910 predicts a response including atranslation such as “Guten Morgen.” And digital assistant 800 providesaudio and/or visual output 1028 including the predicted response.

FIG. 10D illustrates another example of processing an incomplete userrequest for translation. As shown in FIG. 10D, digital assistant 800receives a speech input 1032 such as “Hey Siri, translate.” Similar tothose described above, based on a determination that the received speechinput 1032 represents a user request for translation, digital assistant800 provides a representation of the received speech input 1032 (e.g., atext representation of “Hey Siri, translate.”) to the trainedmachine-learning translation system 910 (e.g., trained with second setof training data 908).

In some embodiments, as described above, to obtain a response, trainedmachine-learning translation system 910 of digital assistant 800determines, without parsing the received speech input to identify ato-be-translated payload, whether the user request for translationincludes at least one to-be-translated payload (e.g., “good morning”)and a target translation language (e.g., “German”). In the example shownin FIG. 10D, machine-learning translation system 910 is trained withsecond set of training data 908, which includes a data item that includeboth the to-be-translated payload and the target translation language(e.g., a text representation of “How do you say ‘Good Morning’ inGerman?”). Trained machine-learning translation system 910 can thusdetermine that the user request for translation included in speech input1032 does not include either the target translation language or theto-be-translated payload.

Based on such a determination, the trained machine-learning translationsystem 910 identifies a third inquiry to obtain the to-be-translatedpayload and the target translation language. As described above in FIG.9C, second set of training data 908 includes data item 938, such as“Sure, what do you want to be translated and into which language?”.Therefore, trained machine-learning translation system 910 identifies athird inquiry similar to data item 938 and digital assistant 800 outputsan audio 1034 of the third inquiry, as shown in FIG. 10D. Digitalassistant 800 receives, for example, third user response 1036 to thethird inquiry (e.g., “I want to translate ‘good morning’ into German.”).Based on third user response 1036, trained machine-learning translationsystem 910 obtains a translation of the to-be-translated payload. Forexample, based on the representation of “I want to translate ‘goodmorning’ into German,” trained machine-learning translation system 910predicts, with high confidence, that the translation is “Guten Morgen.”Accordingly, trained machine-learning translation system 910 predicts aresponse including a translation such as “Guten Morgen.” And digitalassistant 800 provides the an audio and/or visual output 1038 includingthe predicted response.

As shown in FIGS. 10B-10D, machine-learning translation system 910trained with second set of training data 908 can process incompletetranslation request without explicitly identifying payload and/or thetarget translation language. Machine-learning translation system 910 istrained to recognize incomplete translation request and, in response,provides inquires for the missing elements in the translation request.

5. Processes for Training a Machine-Learning Translation System andPerforming Translation Using the Trained Machine-Learning TranslationSystem

FIG. 11A-11B illustrate exemplary process 1100 for training amachine-learning translation system. Process 1100 is performed, forexample, using one or more electronic devices implementing a digitalassistant. In some examples, process 1100 is performed using aclient-server system (e.g., system 100), and the blocks of process 1100are divided up in any manner between the server (e.g., DA server 106)and a client device. In other examples, the blocks of process 1100 aredivided up between the server and multiple client devices. Thus, whileportions of process 1100 are described herein as being performed byparticular devices of a client-server system, it will be appreciatedthat process 1100 is not so limited. In other examples, process 1100 isperformed using only one or more servers, a client device (e.g., userdevice 104, 200, or 400), or only multiple client devices. In process1100, some blocks are, optionally, combined, the order of some blocksis, optionally, changed, and some blocks are, optionally, omitted. Insome examples, additional steps may be performed in combination with theprocess 1100.

As described above, explicitly identifying a translation payload from auser request may sometimes be difficult and inaccurate. The incorrect orinaccurate identification of the translation payload may result in anerroneous translation. The techniques described in this applicationreduce or eliminate the requirement of explicitly identifying atranslation payload from a user request. In one process implementing thetechniques described above, training data are adapted to include payloadformulated as translation requests. Adapting the training data usestemplates that are obtained based on analysis of past translationsrequests and/or other sources (e.g., internal/external online sourcessuch as dictionaries). The adapted training data are used to train amachine-learning translation system so that it can provide an accuratetranslation without having to explicitly identify a payload from a userrequest translation. Adapting the training data therefore improves theaccuracy of translation, improves translation speed, and in turnenhances the overall system performance.

With reference to FIG. 11A, at block 1102, a first set of training data(e.g., first set of training data 902 shown in FIG. 9A) is obtained. Thefirst set of training data includes at least one payload in a firstlanguage (e.g., English) and a translation of the at least one payloadin a second language (e.g., German). In some embodiments, the at leastone payload includes one or more tokens in the first language. The oneor more tokens include at least one of a word, a phrase, and a sentence.In some embodiments, the first set of training data further includes atranslation of the at least one payload in a third language (e.g.,French). Examples of the payloads and translations of the payloads in afirst set of training data are illustrated in FIG. 9B as describedabove.

At block 1104, one or more templates for adapting the at least onepayload are obtained. The one or more templates include one or moreprefixes and one or more variables. In some embodiments, the one or moreprefixes include a first prefix, which includes one or more tokensrepresenting a partial request for translation. The one or more prefixesinclude a second prefix, which includes a trigger phrase for invoking adigital assistant operating on the electronic device. The one or moreprefixes include a third prefix, which includes one or more tokens thatare substantially identical to one or more tokens included in the atleast one payload. In some embodiments, the one or more variablesinclude a first variable representing a to-be-translated payload in thefirst language and a second variable representing a target translationlanguage. Examples of templates are illustrated in FIG. 9B as describedabove (e.g., “How do I say ${payload} in ${target}?”).

At block 1106, the at least one payload is adapted using the one or moretemplates to generate at least one adapted payload formulated as atranslation request. In some embodiments, the at least one payload isadapted by replacing a first variable of the one or more templates withthe at least one payload (block 1108); and replacing a second variableof the one or more templates with the second language (block 1110). Forexample, as described above with respect to FIG. 9B, using template 904A(e.g., “How do I say ${payload} in {target}?”), data item 902A (e.g.,{“Cat”} {“eine Katze”}) in the first training data set 902 is adapted togenerate an adapted data item 924A in second set of training data 908(e.g., {“How do I say cat in German?”} {“eine Katze.”}).

At block 1112, a second set of training data is generated based on theat least one adapted payload. At block 1114, to generate the second setof training data, each of the payload in the first set of training datais replaced with the corresponding adapted payload.

With reference to FIG. 11B, in some embodiments, a user request fortranslation may be incomplete. Examples of incomplete user requests aredescribed above with respect to FIG. 9C. In anticipating an incompleterequest that does not include a target translation language (e.g., “Howdo I say ‘good morning?” ’), at block 1116, a first inquiry to obtain atarget translation language is generated using a first payload of the atleast one payload included in the first set of training data. An exampleof the first inquiry can be inquiry 934A such as “Into which language doyou want ‘good morning’ to be translated?”, as illustrated in FIG. 9Cdescribed above. At block 1118, the generated first inquiry is includedin the second set of training data for training the machine-learningtranslation system.

In anticipating an incomplete request that does not include a payload(e.g., “Can you translate into German?”), at block 1120, a secondinquiry to obtain a to-be-translated payload is generated based on thesecond language. An example of the second inquiry can be inquiry 936such as “Sure, what do you want to be translated into German?”, asillustrated in FIG. 9C described above. At block 1122, the generatedsecond inquiry is generated in the second set of training data fortraining the machine-learning translation system.

In anticipating an incomplete request that does not include either apayload or a target translation language (e.g., “Hey Siri, translate.”),at block 1124, a third inquiry to obtain a to-be-translated payload anda target translation language is generated. An example of the thirdinquiry can be inquiry 938 such as “Sure, what do you want to betranslated and into which language?”, as illustrated in FIG. 9Cdescribed above. At block 1126, the generated third inquiry is includedin the second set of training data.

At block 1128, the machine-learning translation system is trained usingthe second set of training data. In some embodiments, themachine-learning translation system is a neural machine translationsystem. Exemplary machine-learning translation systems and trainingthereof using the second set of training data are described above withrespect to FIG. 9A.

FIGS. 12A-12D illustrate exemplary process 1200 for performingtranslation using a machine-learning translation system. Process 1200 isperformed, for example, using one or more electronic devicesimplementing a digital assistant. In some examples, process 1200 isperformed using a client-server system (e.g., system 100), and theblocks of process 1200 are divided up in any manner between the server(e.g., DA server 106) and a client device. In other examples, the blocksof process 1200 are divided up between the server and multiple clientdevices. Thus, while portions of process 1100 are described herein asbeing performed by particular devices of a client-server system, it willbe appreciated that process 1200 is not so limited. In other examples,process 1200 is performed using only a client device (e.g., user device104, 200, or 400) or only multiple client devices. In process 1200, someblocks are, optionally, combined, the order of some blocks is,optionally, changed, and some blocks are, optionally, omitted. In someexamples, additional steps may be performed in combination with theprocess 1200.

As described above, adapted training data are used to train amachine-learning translation system. In a process implementing thetechniques described above, the trained machine-learning system providesan accurate translation without having to explicitly identify a payloadfrom a user request translation. The machine-learning translationsystems described in this application thus provide an end-to-endsolution that enables a direct processing of the entire user request toprovide the required translation. Further, the machine-learning systemcan also be trained to process incomplete user request. Upon receivingthe training, the machine-learning translation system can respond byoutputting inquires for obtaining the missing information (e.g., missingpayloads or target translation language) and can then perform therequested translation using the user responses to the inquires. Thetrained machine-translation system therefore improves the accuracy oftranslation, improves translation speed, and in turn enhances theoverall system performance.

With reference to FIG. 12A, at block 1202, a speech input (e.g., speechinput 1002 shown in FIG. 10A) is received via a microphone of anelectronic device. At block 1204, in response to receiving the speechinput, whether the received speech input represents a user request fortranslation is determined. In some embodiments, to determine whether thereceived speech input represents a user request for translation, thereceived speech input is interpreted to derive a user intent (block1206); whether the user intent is associated with a translation domainis determined (block 1208); and in accordance with a determination thatthe user intent is associated with a translation domain, the receivedspeech input is determined to represent a user request for translation(block 1210).

At block 1212, in accordance with a determination that the receivedspeech input represents a user request for translation, a representationof the received speech input is provided to the machine-learningtranslation system trained with a set of training data. The set oftraining data comprises at least one adapted payload formulated as atranslation request. Examples of a trained machine-learning translationsystem is described above with respect to FIG. 10A.

At block 1214, using the trained machine-learning translation system, aresponse to the user request for translation is obtained based on therepresentation of the received speech input. As described above withrespect to FIG. 10A, a trained machine-learning translation system 910can process an explicit translation request such as “How do you say‘Good morning’ in German?”, and process an implicit translation request(e.g., “How do I order a Margarita in Spanish?”) without parsing thetranslation request to explicitly identify the to-be-translated payloadand/or the target translation language.

In some embodiments, at block 1216, to obtain the response to the userrequest for translation, whether the user request for translationincludes at least one to-be-translated payload (e.g., “good morning”)and a single target translation language (e.g., German) is determinedwithout parsing the received speech input to identify a to-be-translatedpayload. For example, the determination can be made using the trainedmachine-learning translation system.

With reference to FIG. 12B, at block 1218, in accordance with adetermination that the user request for translation includes at leastone to-be-translated payload and a single target translation language, atranslation (e.g., “Guten Morgen”) of the at least one to-be-translatedpayload is obtained.

As described above, in some embodiments, the user request fortranslation may be incomplete. At block 1220, in accordance with adetermination that the user request for translation (e.g., “How do yousay ‘good morning’?”) includes at least one to-be-translated payload anddoes not include a single target translation language, a first inquiryto obtain the single target translation language is identified using themachine-learning translation system (block 1222). Examples ofidentifying a first inquiry (e.g., “Into which language do you want‘good morning’ to be translated?”) are described above with respect toFIG. 10B. At block 1224, an audio of the first inquiry is outputted. Atblock 1226, a first user response to the first inquiry is received(e.g., German). At block 1228, a translation (e.g., “Guten Morgen.”) ofthe at least one to-be-translated payload is obtained based on the firstuser response.

With reference to FIG. 12C, at block 1230, in accordance with adetermination that the user request for translation (e.g., “Can youtranslate into German?”) includes a single target translation languageand does not include at least one to-be-translated payload, a secondinquiry to obtain the to-be-translated payload is identified using themachine-learning translation system (block 1232). Examples ofidentifying a second inquiry (e.g., “Sure, what do you want to betranslated into German?”) are described above with respect to FIG. 10C.At block 1234, an audio of the second inquiry is outputted. At block1236, a second user response (e.g., “Good morning.”) to the secondinquiry is received. At block 1238, a translation (e.g., “Guten Morgen”)of the at least one to-be-translated payload is obtained based on thesecond user response.

At block 1240, in accordance with a determination that the user requestfor translation (e.g., “Hey Siri, translate.”) does not include at leastone to-be-translated payload and does not include a single targettranslation language, a third inquiry to obtain the at least oneto-be-translated payload and the single target translation language isidentified using the machine-learning translation system (block 1242).Examples of identifying a third inquiry (e.g., “Sure, what do you wantto be translated and into which language?”) are described above withrespect to FIG. 10D. With reference to FIG. 12D, at block 1244, an audioof the third inquiry is outputted. At block 1246, a third user response(e.g., “I want to translate ‘good morning’ into German.”) to the thirdinquiry is received. At block 1248, a translation (e.g., “Guten Morgen”)of the at least one to-be-translated payload is obtained based on thethird user response.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the techniques and their practical applications. Othersskilled in the art are thereby enabled to best utilize the techniquesand various embodiments with various modifications as are suited to theparticular use contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

As described above, one aspect of the present technology is thegathering and use of data available from various sources (e.g., pastuser translation requests) and generate training data for improvinghuman-machine interface to provide a more accurate translation. Thepresent disclosure contemplates that in some instances, this gathereddata may include personal information data that uniquely identifies orcan be used to contact or locate a specific person. Such personalinformation data can include personal interests, demographic data,location-based data, telephone numbers, email addresses, twitter IDs,home addresses, data or records relating to a user's health or level offitness (e.g., vital signs measurements, medication information,exercise information), date of birth, or any other identifyinginformation.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used toprovide a translation that is more customized to the user. Accordingly,use of such personal information data enables calculated control of theprovided translation. Further, other uses for personal information datathat benefit the user are also contemplated by the present disclosure.For instance, health and fitness data may be used to provide insightsinto a user's general wellness, or may be used as positive feedback toindividuals using technology to pursue wellness goals.

The present disclosure further contemplates that the entitiesresponsible for the collection, analysis, disclosure, transfer, storage,or other use of such personal information data will comply withwell-established privacy policies and/or privacy practices. Inparticular, such entities should implement and consistently use privacypolicies and practices that are generally recognized as meeting orexceeding industry or governmental requirements for maintaining personalinformation data private and secure. Such policies should be easilyaccessible by users, and should be updated as the collection and/or useof data changes. Personal information from users should be collected forlegitimate and reasonable uses of the entity and not shared or soldoutside of those legitimate uses. Further, such collection/sharingshould occur only after receiving the informed consent of the users.Additionally, such entities should consider taking any needed steps forsafeguarding and securing access to such personal information data andensuring that others with access to the personal information data adhereto their privacy policies and procedures. Further, such entities cansubject themselves to evaluation by third parties to certify theiradherence to widely accepted privacy policies and practices. Inaddition, policies and practices should be adapted for the particulartypes of personal information data being collected and/or accessed andadapted to applicable laws and standards, includingjurisdiction-specific considerations. For instance, in the US,collection of or access to certain health data may be governed byfederal and/or state laws, such as the Health Insurance Portability andAccountability Act (HIPAA); whereas health data in other countries maybe subject to other regulations and policies and should be handledaccordingly. Hence different privacy practices should be maintained fordifferent personal data types in each country.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof collecting past user translation requests, the present technology canbe configured to allow users to select to “opt in” or “opt out” ofparticipation in the collection of personal information data during thecollections of past user translation requests. In another example, userscan select not to provide past user translation requests. In yet anotherexample, users can select to limit the length of time collected pastuser translation requests are maintained or entirely prohibit thegeneration of training data based on collected past user translationrequests. In addition to providing “opt in” and “opt out” options, thepresent disclosure contemplates providing notifications relating to theaccess or use of personal information. For instance, a user may benotified upon downloading an app that their personal information datawill be accessed and then reminded again just before personalinformation data is accessed by the app.

Moreover, it is the intent of the present disclosure that personalinformation data should be managed and handled in a way to minimizerisks of unintentional or unauthorized access or use. Risk can beminimized by limiting the collection of data and deleting data once itis no longer needed. In addition, and when applicable, including incertain health related applications, data de-identification can be usedto protect a user's privacy. De-identification may be facilitated, whenappropriate, by removing specific identifiers (e.g., date of birth,etc.), controlling the amount or specificity of data stored (e.g.,collecting location data at a city level rather than at an addresslevel), controlling how data is stored (e.g., aggregating data acrossusers), and/or other methods.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, training datacan be adapted based on non-personal information data or a bare minimumamount of personal information, such as the content being requested bythe device associated with a user, other non-personal informationavailable to the translation services, or publically availableinformation.

What is claimed is:
 1. An electronic device, comprising: one or moreprocessors; a microphone; and memory storing one or more programsconfigured to be executed by the one or more processors, the one or moreprograms including instructions for: receiving, via a microphone, aspeech input; in response to receiving the speech input, determiningwhether the received speech input represents a user request fortranslation; in accordance with a determination that the received speechinput represents a user request for translation, providing arepresentation of the received speech input to a machine-learningtranslation system trained with a set of training data, wherein the setof training data comprises at least one adapted payload formulated as atranslation request, wherein the at least one adapted payload formulatedas a translation request is adapted by: obtaining a first set of dataincluding at least one payload in at least one source language and atleast one corresponding translation into at least one target language;and using at least one template configured to facilitate formulatingeach of the at least one payload in the first set of data as atranslation request from a corresponding source language to acorresponding target language, generating the set of training dataincluding the at least one adapted payload; obtaining, using the trainedmachine-learning translation system, a response to the user request fortranslation based on the representation of the received speech input;and providing an audio output corresponding to the obtained response tothe user request for translation.
 2. The electronic device of claim 1,wherein determining whether the received speech input represents a userrequest for translation comprises: interpreting the received speechinput to derive a user intent; determining whether the user intent isassociated with a translation domain; and in accordance with adetermination that the user intent is associated with a translationdomain, determining that the received speech input represents a userrequest for translation.
 3. The electronic device of claim 1, whereinobtaining, using the trained machine-learning translation system, aresponse to the user request for translation based on the representationof the received speech input comprises: determining, without parsing thereceived speech input to identify a to-be-translated payload, whetherthe user request for translation includes at least one to-be-translatedpayload and a single target translation language; and in accordance witha determination that the user request for translation includes at leastone to-be-translated payload and a single target translation language,obtaining a translation of the at least one to-be-translated payload. 4.The electronic device of claim 3, wherein the one or more programscomprise further instructions for: in accordance with a determinationthat the user request for translation includes at least oneto-be-translated payload and does not include a single targettranslation language, identifying, using the machine-learningtranslation system, a first inquiry to obtain the single targettranslation language; outputting an audio of the first inquiry; andreceiving a first user response to the first inquiry.
 5. The electronicdevice of claim 4, wherein the one or more programs comprise furtherinstructions for: obtaining a translation of the at least oneto-be-translated payload based on the first user response.
 6. Theelectronic device of claim 3, wherein the one or more programs comprisefurther instructions for: in accordance with a determination that theuser request for translation includes a single target translationlanguage and does not include at least one to-be-translated payload,identifying, using the machine-learning translation system, a secondinquiry to obtain the to-be-translated payload; outputting an audio ofthe second inquiry; and receiving a second user response to the secondinquiry.
 7. The electronic device of claim 6, wherein the one or moreprograms comprise further instructions for: obtaining a translation ofthe at least one to-be-translated payload based on the second userresponse.
 8. The electronic device of claim 3, wherein the one or moreprograms comprise further instructions for: in accordance with adetermination that the user request for translation does not include atleast one to-be-translated payload and does not include a single targettranslation language, identifying, using the machine-learningtranslation system, a third inquiry to obtain the at least oneto-be-translated payload and the single target translation language;outputting an audio of the third inquiry; and receiving a third userresponse to the third inquiry.
 9. The electronic device of claim 8,wherein the one or more programs comprise further instructions for:obtaining a translation of the at least one to-be-translated payloadbased on the third user response.
 10. A method of performing translationusing a machine-learning translation system, the method comprising: atan electronic device with one or more processors, memory, and amicrophone: receiving, via the microphone, a speech input; in responseto receiving the speech input, determining whether the received speechinput represents a user request for translation; in accordance with adetermination that the received speech input represents a user requestfor translation, providing a representation of the received speech inputto a machine-learning translation system trained with a set of trainingdata, wherein the set of training data comprises at least one adaptedpayload formulated as a translation request, wherein the at least oneadapted payload formulated as a translation request is adapted by:obtaining a first set of data including at least one payload in at leastone source language and at least one corresponding translation into atleast one target language; and using at least one template configured tofacilitate formulating each of the at least one payload in the first setof data as a translation request from a corresponding source language toa corresponding target language, generating the set of training dataincluding the at least one adapted payload; obtaining, using the trainedmachine-learning translation system, a response to the user request fortranslation based on representation of the received speech input; andproviding an audio output corresponding to the obtained response to theuser request for translation.
 11. The method of claim 10, whereindetermining whether the received speech input represents a user requestfor translation comprises: interpreting the received speech input toderive a user intent; determining whether the user intent is associatedwith a translation domain; and in accordance with a determination thatthe user intent is associated with a translation domain, determiningthat the received speech input represents a user request fortranslation.
 12. The method of claim 10, wherein obtaining, using thetrained machine-learning translation system, a response to the userrequest for translation based on the representation of the receivedspeech input comprises: determining, without parsing the received speechinput to identify a to-be-translated payload, whether the user requestfor translation includes at least one to-be-translated payload and asingle target translation language; and in accordance with adetermination that the user request for translation includes at leastone to-be-translated payload and a single target translation language,obtaining a translation of the at least one to-be-translated payload.13. The method of claim 12, further comprising: in accordance with adetermination that the user request for translation includes at leastone to-be-translated payload and does not include a single targettranslation language: identifying, using the machine-learningtranslation system, a first inquiry to obtain the single targettranslation language; outputting an audio of the first inquiry; andreceiving a first user response to the first inquiry.
 14. The method ofclaim 13, further comprising: obtaining a translation of the at leastone to-be-translated payload based on the first user response.
 15. Themethod of claim 12, further comprising: in accordance with adetermination that the user request for translation includes a singletarget translation language and does not include at least oneto-be-translated payload: identifying, using the machine-learningtranslation system, a second inquiry to obtain the to-be-translatedpayload; outputting an audio of the second inquiry; and receiving asecond user response to the second inquiry.
 16. The method of claim 15,further comprising: obtaining a translation of the at least oneto-be-translated payload based on the second user response.
 17. Themethod of claim 12, further comprising: in accordance with adetermination that the user request for translation does not include atleast one to-be-translated payload and does not include a single targettranslation language: identifying, using the machine-learningtranslation system, a third inquiry to obtain the at least oneto-be-translated payload and the single target translation language;outputting an audio of the third inquiry; and receiving a third userresponse to the third inquiry.
 18. The method of claim 17, furthercomprising: obtaining a translation of the at least one to-be-translatedpayload based on the third user response.
 19. A non-transitorycomputer-readable storage medium storing one or more programs configuredto be executed by one or more processors of an electronic device, theone or more programs including instructions for: receiving, via themicrophone, a speech input; in response to receiving the speech input,determining whether the received speech input represents a user requestfor translation; in accordance with a determination that the receivedspeech input represents a user request for translation, providing arepresentation of the received speech input to a machine-learningtranslation system trained with a set of training data, wherein the setof training data comprises at least one adapted payload formulated as atranslation request, wherein the at least one adapted payload formulatedas a translation request is adapted by: obtaining a first set of dataincluding at least one payload in at least one source language and atleast one corresponding translation into at least one target language;and using at least one template configured to facilitate formulatingeach of the at least one payload in the first set of data as atranslation request from a corresponding source language to acorresponding target language, generating the set of training dataincluding the at least one adapted payload; obtaining, using the trainedmachine-learning translation system, a response to the user request fortranslation based on the representation of the received speech input;and providing an audio output corresponding to the obtained response tothe user request for translation.
 20. The non-transitorycomputer-readable storage medium of claim 19, wherein determiningwhether the received speech input represents a user request fortranslation comprises: interpreting the received speech input to derivea user intent; determining whether the user intent is associated with atranslation domain; and in accordance with a determination that the userintent is associated with a translation domain, determining that thereceived speech input represents a user request for translation.
 21. Thenon-transitory computer-readable storage medium of claim 19, whereinobtaining, using the trained machine-learning translation system, aresponse to the user request for translation based on the representationof the received speech input comprises: determining, without parsing thereceived speech input to identify a to-be-translated payload, whetherthe user request for translation includes at least one to-be-translatedpayload and a single target translation language; and in accordance witha determination that the user request for translation includes at leastone to-be-translated payload and a single target translation language,obtaining a translation of the at least one to-be-translated payload.22. The non-transitory computer-readable storage medium of claim 20,wherein the one or more programs comprise further instructions for: inaccordance with a determination that the user request for translationincludes at least one to-be-translated payload and does not include asingle target translation language, identifying, using themachine-learning translation system, a first inquiry to obtain thesingle target translation language; outputting an audio of the firstinquiry; and receiving a first user response to the first inquiry. 23.The non-transitory computer-readable storage medium of claim 22, whereinthe one or more programs comprise further instructions for: obtaining atranslation of the at least one to-be-translated payload based on thefirst user response.
 24. The non-transitory computer-readable storagemedium of claim 20, wherein the one or more programs comprise furtherinstructions for: in accordance with a determination that the userrequest for translation includes a single target translation languageand does not include at least one to-be-translated payload, identifying,using the machine-learning translation system, a second inquiry toobtain the to-be-translated payload; outputting an audio of the secondinquiry; and receiving a second user response to the second inquiry. 25.The non-transitory computer-readable storage medium of claim 24, whereinthe one or more programs comprise further instructions for: obtaining atranslation of the at least one to-be-translated payload based on thesecond user response.
 26. The non-transitory computer-readable storagemedium of claim 20, wherein the one or more programs comprise furtherinstructions for: in accordance with a determination that the userrequest for translation does not include at least one to-be-translatedpayload and does not include a single target translation language,identifying, using the machine-learning translation system, a thirdinquiry to obtain the at least one to-be-translated payload and thesingle target translation language; outputting an audio of the thirdinquiry; and receiving a third user response to the third inquiry. 27.The non-transitory computer-readable storage medium of claim 26, whereinthe one or more programs comprise further instructions for: obtaining atranslation of the at least one to-be-translated payload based on thethird user response.